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Internet Engineering Task Force
INTERNET-DRAFT
TE Working Group
                                          Daniel O. Awduche
July 2000                                 UUNET (Worldcom)

                                          Angela Chiu
                                          AT&T

                                          Anwar Elwalid
                                          Lucent Technologies

                                          Indra Widjaja
                                          Fujitsu Network Communications

                                          Xipeng Xiao
                                          Global Crossing


              A Framework for Internet Traffic Engineering

                    draft-ietf-tewg-framework-02.txt


Status of this Memo

   This document is an Internet-Draft and is in full conformance with
   all provisions of Section 10 of RFC2026.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF), its areas, and its working groups.  Note that
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   Drafts.

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     The list of current Internet-Drafts can be accessed at
     http://www.ietf.org/ietf/1id-abstracts.txt

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Abstract


   This memo describes a framework for Traffic Engineering (TE) in the
   Internet.  The framework is intended to promote better understanding
   of the issues surrounding traffic engineering in IP networks, and to
   provide a common basis for the development of traffic engineering
   capabilities for the Internet.  The principles, architectures, and
   methodologies for performance evaluation and performance optimization
   of operational IP networks are discussed throughout this document.
   The optimization goals of traffic engineering are to enhance the
   performance of IP traffic while utilizing network resources
   economically and reliably. The framework includes a set of generic
   requirements, recommendations, and options for Internet traffic
   engineering.  The framework can serve as a guide to implementors of
   online and offline Internet traffic engineering mechanisms, tools,
   and support systems. The framework can also help service providers
   devise traffic engineering solutions for their networks.


Table of Contents

    1.0 Introduction
       1.1 What is Internet Traffic Engineering?
       1.2 Scope
       1.3 Terminology
    2.0 Background
       2.1 Context of Internet Traffic Engineering
       2.2 Network Context
       2.3 Problem Context
          2.3.1 Congestion and its Ramifications
       2.4 Solution Context
          2.4.1 Combating the Congestion Problem
       2.5 Implementation and Operational Context
    3.0 Traffic Engineering Process Model
       3.1 Components of the Traffic Engineering Process Model
       3.2 Measurement
       3.3 Modeling, Analysis, and Simulation
       3.4 Optimization
    4.0 Historical Review and Recent Developments
       4.1 Traffic Engineering in Classical Telephone Networks
       4.2 Evolution of Traffic Engineering in the Internet
          4.2.1 Adaptive Routing in ARPANET
          4.2.2 Dynamic Routing in the Internet
          4.2.3 ToS Routing
          4.2.4 Equal Cost MultiPath
          4.2.5 Nimrod
       4.3 Overlay Model
       4.4 Constraint-Based Routing
       4.5 Overview of Other IETF Projects Related to Traffic
             Engineering
          4.5.1 Integrated Services
          4.5.2 RSVP
          4.5.3 Differentiated Services



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          4.5.4 MPLS
          4.5.5 IP Performance Metrics
          4.5.6 Flow Measurement
          4.5.7 Endpoint Congestion Management
       4.6 Overview of ITU Activities Related to Traffic
            Engineering
    5.0 Taxonomy of Traffic Engineering Systems
       5.1 Time-Dependent Versus State-Dependent
       5.2 Offline Versus Online
       5.3 Centralized Versus Distributed
       5.4 Local Versus Global
       5.5 Prescriptive Versus Descriptive
       5.6 Open-Loop Versus Closed-Loop
       5.7 Tactical vs Strategic
    6.0 Requirements for Internet Traffic Engineering
       6.1 Generic Requirements
       6.2 Routing Requirements
       6.3 Traffic Mapping Requirements
       6.4 Measurement Requirements
       6.5 Network Survivability
          6.5.1 Survivability in MPLS Based Networks
          6.5.2 Protection Option
          6.5.3 Resilience Attributes
       6.6 Content Distribution (Webserver) Requirements
       6.7 Traffic Engineering in Diffserv Environments
       6.8 Network Controllability
    7.0 Inter-Domain Considerations
    8.0 Overview of Contemporary TE Practices in Operational
         IP Networks
    9.0 Conclusion
    10.0 Security Considerations
    11.0 Acknowledgments
    12.0 References
    13.0 Authors' Addresses


1.0 Introduction


   This memo describes a framework for Internet traffic engineering.
   The objective of the document is to articulate the general issues,
   principles and requirements for Internet traffic engineering; and
   where appropriate to provide recommendations, guidelines, and options
   for the development of online and offline Internet traffic
   engineering capabilities and support systems.

   The framework can aid service providers in devising and implementing
   traffic engineering solutions for their networks. Networking hardware
   and software vendors will also find the framework helpful in the
   development of mechanisms and support systems for the Internet
   environment that support the traffic engineering function.

   The framework provides a terminology for describing and understanding
   common Internet traffic engineering concepts.  The framework also



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   provides a taxonomy of known traffic engineering styles.  In this
   context, a traffic engineering style abstracts important aspects from
   a traffic engineering methodology. Traffic engineering styles can be
   viewed in different ways depending upon the specific context in which
   they are used and the specific purpose which they serve. The
   combination of styles and views results in a natural taxonomy of
   traffic engineering systems.

   Even though Internet traffic engineering is most effective when
   applied end-to-end, the initial focus of this framework document is
   intra-domain traffic engineering (that is, traffic engineering within
   a given autonomous system). However, because a preponderance of
   Internet traffic tends to be inter-domain (originating in one
   autonomous system and terminating in another), this document provides
   an overview of aspects pertaining to inter-domain traffic
   engineering.

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED",  "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119.


1.1. What is Internet Traffic Engineering?


   Internet traffic engineering is defined as that aspect of Internet
   network engineering dealing with the issue of performance evaluation
   and performance optimization of operational IP networks. Traffic
   Engineering encompasses the application of technology and scientific
   principles to the measurement, characterization, modeling, and
   control of Internet traffic [AWD1, AWD2].

   Enhancing the performance of an operational network, at both the
   traffic and resource levels, are major objectives of Internet traffic
   engineering. This is accomplished by addressing traffic oriented
   performance requirements, while utilizing network resources
   economically and reliably. Traffic oriented performance measures
   include delay, delay variation, packet loss, and goodput.

   An important objective of Internet traffic engineering is to
   facilitate reliable network operations [AWD1]. Reliable network
   operations can be facilitated by providing mechanisms that enhance
   network integrity and by embracing policies emphasizing network
   survivability. This results in a minimization of the vulnerability of
   the network to service outages arising from errors, faults, and
   failures occurring within the infrastructure.

   An Internet exists in order to transfer information from source nodes
   to destination nodes. Accordingly, one of the most significant
   functions performed by an Internet is the routing of traffic from
   ingress nodes to egress nodes. Therefore, one of the most distinctive
   functions performed by Internet traffic engineering is the control
   and optimization of the routing function, to steer traffic through
   the network in the most effective way.



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   Ultimately, it is the performance of the network as seen by end users
   of network services that is truly paramount. This crucial point
   should be considered throughout the development of traffic
   engineering mechanisms and policies. The characteristics visible to
   end users are the emergent properties of the network, which are the
   characteristics of the network when viewed as a whole. A central goal
   of the service provider, therefore, is to enhance the emergent
   properties of the network while taking economic considerations into
   account.

   The importance of the above observation regarding the emergent
   properties of networks is that special care must be taken when
   choosing network performance measures to optimize. Optimizing the
   wrong measures may achieve certain local objectives, but may have
   disastrous consequences on the emergent properties of the network and
   thereby on the quality of service perceived by end-users of network
   services.

   A subtle, but practical advantage of the systematic application of
   traffic engineering concepts to operational networks is that it helps
   to identify and structure goals and priorities in terms of enhancing
   the quality of service delivered to end-users of network services.
   The application of traffic engineering concepts also aids in the
   measurement and analysis of the achievement of these goals.

   The optimization aspects of traffic engineering can be achieved
   through capacity management and traffic management. As used in this
   document, capacity management includes capacity planning, routing
   control, and resource management. Network resources of particular
   interest include link bandwidth, buffer space, and computational
   resources. Likewise, as used in this document, traffic management
   includes (1) nodal traffic control functions such as traffic
   conditioning, queue management, scheduling, and (2) other functions
   that regulate traffic flow through the network or that arbitrate
   access to network resources between different packets or between
   different traffic streams.

   The optimization objectives of Internet traffic engineering should be
   viewed as a continual and iterative process of network performance
   improvement and not simply as a one time goal. Traffic engineering
   also demands continual development of new technologies and new
   methodologies for network performance enhancement.

   The optimization objectives of Internet traffic engineering may
   change over time as new requirements are imposed, as new technologies
   emerge, or as new insights are brought to bear on the underlying
   problems. Moreover, different networks may have different
   optimization objectives, depending upon their business models,
   capabilities, and operating constraints. The optimization aspects of
   traffic engineering are ultimately concerned with network control
   regardless of the specific optimization goals in any particular
   environment.

   Thus, the optimization aspects of traffic engineering can be viewed



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   from a control perspective. The aspect of control within the Internet
   traffic engineering arena can be pro-active and/or reactive. In the
   pro-active case, the traffic engineering control system takes
   preventive action to obviate predicted unfavorable future network
   states.  It may also take perfective action to induce a more
   desirable state in the future. In the reactive case, the control
   system responds correctively and perhaps adaptively to events that
   have already transpired in the network.

   The control dimension of Internet traffic engineering responds at
   multiple levels of temporal resolution to network events. Certain
   aspects of capacity management, such as capacity planning, respond at
   very coarse temporal levels, ranging from days to possibly years. The
   introduction of automatically switched optical transport networks
   (e.g. based on the Multiprotocol Lambda Switching concepts [AWD6])
   could significantly reduce the lifecycle for capacity planning by
   expediting provisioning of optical bandwidth. Routing control
   functions operate at intermediate levels of temporal resolution,
   ranging from milliseconds to days.  Finally, the packet level
   processing functions (e.g. rate shaping, queue management, and
   scheduling) operate at very fine levels of temporal resolution,
   ranging from picoseconds to milliseconds while responding to the
   real-time statistical behavior of traffic. The subsystems of Internet
   traffic engineering control include: capacity augmentation, routing
   control, traffic control, and resource control (including control of
   service policies at network elements). When capacity is to be
   augmented for tactical purposes, it may be desirable to devise a
   deployment plan expedites bandwidth provisioning while minimizing
   installation costs.

   Inputs into the traffic engineering control system include network
   state variables, policy variables, and decision variables.

   One major challenge of Internet traffic engineering is the
   realization of automated control capabilities that adapt quickly and
   cost effectively to significant changes in a network's state, while
   still maintaining stability.

   Another critical dimension of Internet traffic engineering is network
   performance evaluation, which is important for assessing the
   effectiveness of traffic engineering methods, and for monitoring and
   verifying compliance with network performance goals.  Results from
   performance evaluation can be used to identify existing problems,
   guide network re-optimization, and aid in the prediction of potential
   future problems.

   Performance evaluation can be achieved in many different ways. The
   most notable techniques include analytical methods, simulation, and
   empirical methods based on measurements.  When analytical methods or
   simulation are used, network nodes and links can be modeled to
   capture relevant operational features such as topology, bandwidth,
   buffer space, and nodal service policies (link scheduling, packet
   prioritization, buffer management, etc). Analytical traffic models
   can be used to depict dynamic and behavioral traffic characteristics,



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   such as burstiness, statistical distributions, dependence, and
   seasonality.

   Performance evaluation can be quite complicated in practical network
   contexts. A number of techniques can be used to simplify the
   analysis, such as abstraction, decomposition, and approximation. For
   example, simplifying concepts such as effective bandwidth and
   effective buffer [Elwalid] may be used to approximate nodal behaviors
   at the packet level and simplify the analysis at the connection
   level. Network analysis techniques using, for example, queuing models
   and approximation schemes based on asymptotic and decomposition
   techniques can render the analysis even more tractable.  In
   particular, an emerging set of concepts known as network calculus
   [Cruz] based on deterministic bounds may simplify network analysis
   relative to classical stochastic techniques. When using analytical
   techniques, care should be taken to ensure that the models faithfully
   reflect the relevant operational characteristics of the modeled
   network entities.

   Simulation can be used to evaluate network performance or to verify
   and validate analytical approximations. Simulation can, however, be
   computationally costly and may not always provide sufficient
   insights. An appropriate approach to a given network performance
   evaluation problem may involve a hybrid combination of analytical
   techniques, simulation, and empirical methods.

   As a general rule, traffic engineering concepts and mechanisms must
   be sufficiently specific and well defined to address known
   requirements, but simultaneously flexible and extensible to
   accommodate unforeseen future demands.


1.2. Scope


   The scope of this document is intra-domain traffic engineering; that
   is, traffic engineering within a given autonomous system in the
   Internet. The framework will discuss concepts pertaining to intra-
   domain traffic control, including such issues as routing control,
   micro and macro resource allocation, and the control coordination
   problems that arise consequently.

   This document will describe and characterize techniques already in
   use or in advanced development for Internet traffic engineering. The
   way these techniques fit together will be discussed and scenarios in
   which they are useful will be identified.

   Although the emphasis is on intra-domain traffic engineering, in
   Section 7.0, however, an overview of the high level considerations
   pertaining to inter-domain traffic engineering will be provided.
   Inter-domain Internet traffic engineering is crucial to the
   performance enhancement of the global Internet infrastructure.

   Whenever possible, relevant requirements from existing IETF documents



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   and other sources will be incorporated by reference.


1.3 Terminology


   This subsection provides terminology which is useful for Internet
   traffic engineering. The definitions presented apply to this
   framework document. These terms may have other meanings elsewhere.

     - Baseline analysis:
          A study conducted to serve as a baseline for comparison to the
          actual behavior of the network.

     - Busy hour:
          A one hour period within a specified interval of time
          (typically 24 hours) in which the traffic load in a
          network or subnetwork is greatest.

      - Bottleneck
          A network element whose input traffic rate tends to be greater
          than its output rate.

      - Congestion:
          A state of a network resource in which the traffic incident
          on the resource exceeds its output capacity over an interval
          of time.

     - Congestion avoidance:
          An approach to congestion management that attempts to obviate
          the occurrence of congestion.

     - Congestion control:
          An approach to congestion management that attempts to remedy
          congestion problems that have already occurred.

     - Constraint-based routing:
          A class of routing protocols that take specified traffic
          attributes, network constraints, and policy constraints into
          account in making routing decisions. Constraint-based routing
          is applicable to traffic aggregates as well as flows. It is a
          generalization of QoS routing.

     - Demand side congestion management:
          A congestion management scheme that addresses congestion
          problems by regulating or conditioning offered load.

     - Effective bandwidth:
          The minimum amount of bandwidth that can be assigned to a flow
          or traffic aggregate in order to deliver 'acceptable service
          quality' to the flow or traffic aggregate.

     - Egress traffic:
          Traffic exiting a network or network element.



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     - Hot-spot
          A network element or subsystem which is in a state of
          congestion.

     - Ingress traffic:
          Traffic entering a network or network element.

     - Inter-domain traffic:
          Traffic that originates in one Autonomous system and
          terminates in another.

     - Loss network:
          A network that does not provide adequate buffering for
          traffic, so that traffic entering a busy resource within
          the network will be dropped rather than queued.

     - Metric:
          A parameter defined in terms of standard units of
          measurement.

     - Measurement Methodology:
          A repeatable measurement technique used to derive one or
          more metrics of interest.

     - Network Survivability:
          The capability to provide a prescribed level of QoS for
          existing services after a given number of failures occur
          within the network.

     - Offline traffic engineering:
          A traffic engineering system that exists outside of the
          network.

     - Online traffic engineering:
          A traffic engineering system that exists within the network,
          typically implemented on or as adjuncts to operational network
          elements.

     - Performance measures:
          Metrics that provide quantitative or qualitative measures of
          the performance of systems or subsystems of interest.

     - Performance management:
          A systematic approach to improving effectiveness in the
          accomplishment of specific networking goals related to
          performance improvement.

     - Performance Metric:
          A performance parameter defined in terms of standard units of
          measurement.

     - Provisioning:
          The process of assigning or configuring network resources to
          meet certain requests.



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     - QoS routing:
          Class of routing systems that selects paths to be used by a
          flow based on the QoS requirements of the flow.

     - Service Level Agreement:
          A contract between a provider and a customer that guarantees
          specific levels of performance and reliability at a certain
          cost.

     - Stability:
          An operational state in which a network does not oscillate
          in a disruptive manner from one mode to another mode.

     - Supply side congestion management:
          A congestion management scheme that provisions additional
          network resources to address existing and/or anticipated
          congestion problems.

     - Transit traffic:
          Traffic whose origin and destination are both outside of
          the network under consideration.

     - Traffic characteristic:
          A description of the temporal behavior or a description of the
          attributes of a given traffic flow or traffic aggregate.

     - Traffic engineering system
          A collection of objects, mechanisms, and protocols that are
          used conjunctively to accomplish traffic engineering
          objectives.

     - Traffic flow:
          A stream of packets between two end-points that can be
          characterized in a certain way. A micro-flow has a more
          specific definition: A micro-flow is a stream of packets with
          a bounded inter-arrival time and with the same source and
          destination addresses, source and destination ports, and
          protocol ID.

     - Traffic intensity:
          A measure of traffic loading with respect to a resource
          capacity over a specified period of time. In classical
          telephony systems, traffic intensity is measured in units of
          Erlang.

     - Traffic matrix:
          A representation of the traffic demand between a set of origin
          and destination abstract nodes. An abstract node can consist
          of one or more network elements.

     - Traffic monitoring:

          The process of observing traffic characteristics at a given
          point in a network and collecting the traffic information for



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          analysis and further action.

     - Traffic trunk:
          An aggregation of traffic flows belonging to the same class
          which are forwarded through a common path. A traffic trunk
          may be characterized by an ingress and egress node, and a
          set of attributes which determine its behavioral
          characteristics and requirements from the network.


2.0 Background


   The Internet has quickly evolved into a very critical communications
   infrastructure, supporting significant economic, educational, and
   social activities. Simultaneously, the delivery of Internet
   communications services has become very competitive and end-users are
   demanding very high quality service from their service providers.
   Consequently, performance optimization of large scale IP networks,
   especially public Internet backbones, has become an important
   problem.  Network performance requirements are multidimensional,
   complex, and sometimes contradictory; making the traffic engineering
   problem very challenging.

   The network must convey IP packets from ingress nodes to egress nodes
   efficiently, expeditiously, reliably, and economically. Furthermore,
   in a multiclass service environment (e.g. Diffserv capable networks),
   the resource sharing parameters of the network must be appropriately
   determined and configured according to prevailing policies and
   service models to resolve resource contention issues arising from
   mutual interference between packets traversing through the network.
   Thus, consideration must be given to resolving competition for
   network resources between traffic streams belonging to the same
   service class (intra-class contention resolution) and traffic streams
   belonging to different classes (inter-class contention resolution).


2.1 Context of Internet Traffic Engineering


   The context of Internet traffic engineering pertains to the scenarios
   in which the problems that traffic engineering attempts to solve
   manifest. A traffic engineering methodology establishes appropriate
   rules to resolve traffic performance issues occurring in a specific
   context. The context of Internet traffic engineering includes:

    (1) A network context defining the universe of discourse,
        and in particular the situations in which the traffic
        engineering problems occur. The network context
        encompasses network structure, network policies, network
        characteristics, network constraints, network quality
        attributes, network optimization criteria, etc.

    (2) A problem context defining the general and concrete



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        issues that traffic engineering addresses. The problem
        context encompasses identification, abstraction of relevant
        features, representation, formulation, specification of
        the requirements on the solution space, specification
        of the desirable features of acceptable solutions, etc.

    (3) A solution context suggesting how to solve the traffic
        engineering problems. The solution context encompasses
        analysis, evaluation of alternatives, prescription, and
        resolution.

    (4) An implementation and operational context in which the
        solutions are methodologically instantiated. The
        implementation and operational context encompasses
        planning, organization, and execution.

   The context of Internet traffic engineering and the different problem
   scenarios are discussed in the following subsections.


2.2 Network Context


   IP networks range in size from small clusters of routers situated
   within a given location, to thousands of interconnected routers,
   switches, and other components distributed all over the world.

   Conceptually, at the most basic level of abstraction, an IP network
   can be represented as a distributed dynamical system consisting of:
   (1) a set of interconnected resources which provide transport
   services for IP traffic subject to certain constraints, (2) a demand
   system representing the offered load to be transported through the
   network, and (3) a response system consisting of network processes,
   protocols, and related mechanisms which facilitate the movement of
   traffic through the network [see also AWD2].

   The network elements and resources may have specific characteristics
   restricting the manner in which the demand is handled. Additionally,
   network resources may be equipped with traffic control mechanisms
   superintending the way in which the demand is serviced.  Traffic
   control mechanisms may, for example, be used to control various
   packet processing activities within a given resource, arbitrate
   contention for access to the resource by different packets, and
   regulate traffic behavior through the resource. A configuration
   management and provisioning system may allow the settings of the
   traffic control mechanisms to be manipulated by external or internal
   entities in order to exercise control over the way in which the
   network elements respond to internal and external stimuli.

   The details of how the network provides transport services for
   packets are specified in the policies of the network administrators
   and are installed through network configuration management and policy
   based provisioning systems.  Generally, the types of services
   provided by the network also depends upon the technology and



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   characteristics of the network elements and protocols, the prevailing
   service and utility models, and the ability of the network
   administrators to translate policies into network configurations.

   Contemporary Internet networks have three significant
   characteristics:  (1) they provide real-time services, (2) they have
   become mission critical, and (3) their operating environments are
   very dynamic. The dynamic characteristics of IP networks can be
   attributed in part to fluctuations in demand, to the interaction
   between various network protocols and processes, to the rapid
   evolution of the infrastructure which demands the constant inclusion
   of new technologies and new network elements, and to transient and
   persistent impairments which occur within the system.

   Packets contend for the use of network resources as they are conveyed
   through the network.  A network resource is considered to be
   congested if the arrival rate of packets exceed the output capacity
   of the resource over an interval of time. Congestion may result in
   some of the arrival packets being delayed or even dropped.
   Congestion increases transit delays, delay variation, packet loss,
   and reduces the predictability of network services. Clearly,
   congestion is a highly undesirable phenomenon.

   Combating congestion at reasonable cost is a major objective of
   Internet traffic engineering.

   Efficient sharing of network resources by multiple traffic streams is
   a basic economic premise for packet switched networks in general and
   the Internet in particular.  A fundamental challenge in network
   operation, especially in a large scale public IP network, is to
   increase the efficiency of resource utilization while minimizing the
   possibility of congestion.

   Increasingly, the Internet will have to function in the presence of
   different classes of traffic with different service requirements. The
   advent of differentiated services makes this requirement particularly
   acute. Thus, packets may be grouped into behavior aggregates such
   that each behavior aggregate may have a common set of behavioral
   characteristics or a common set of delivery requirements. In
   practice, the delivery requirements of a specific set of packets may
   be specified explicitly or implicitly. Two of the most important
   traffic delivery requirements are capacity constraints and QoS
   constraints.

   Capacity constraints can be expressed statistically as peak rates,
   mean rates, burst sizes, or as some deterministic notion of effective
   bandwidth.  QoS requirements can be expressed in terms of (1)
   integrity constraints such as packet loss and (2) in terms of
   temporal constraints such as timing restrictions for the delivery of
   each packet (delay) and  timing restrictions for the delivery of
   consecutive packets belonging to the same traffic stream (delay
   variation).





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2.3 Problem Context


   Fundamental problems exist in association with the operation of a
   network described by the simple model of the previous subsection.
   This subsection reviews the problem context in relation to the
   traffic engineering function.

   The identification, abstraction, representation, and measurement of
   network features relevant to traffic engineering is a significant
   issue.

   One particularly important class of problems concerns how to
   explicitly formulate the problems that traffic engineering attempts
   to solve, how to identify the requirements on the solution space, how
   to specify the desirable features of good solutions, how to actually
   solve the problems, and how to measure and characterize the
   effectiveness of the solutions.

   Another class of problems concerns how to measure and estimate
   relevant network state parameters. Effective traffic engineering
   relies on a good estimate of the offered traffic load as well as a
   view of the underlying topology and associated resource constraints.
   A network-wide view of the topology is also a must for offline
   planning.

   Still another class of problems concerns how to characterize the
   state of the network and how to evaluate its performance under a
   variety of scenarios. The performance evaluation problem is two-fold.
   One aspect of this problem relates to the evaluation of the system
   level performance of the network. The other aspect relates to the
   evaluation of the resource level performance, which restricts
   attention to the performance analysis of individual network
   resources. In this memo, we shall refer to the system level
   characteristics of the network as the "macro-states" and the resource
   level characteristics as the "micro-states." The system level
   characteristics are also known as the emergent properties of the
   network as noted earlier.  Correspondingly, we shall refer to the
   traffic engineering schemes dealing with network performance
   optimization at the systems level as "macro-TE" and the schemes that
   optimize at the individual resource level as "micro-TE."  Under
   certain circumstances, the system level performance can be derived
   from the resource level performance using appropriate rules of
   composition, depending upon the particular performance measures of
   interest.

   Another fundamental class of problems concerns how to effectively
   optimize network performance. Performance optimization may entail
   translating solutions to specific traffic engineering problems into
   network configurations. Optimization may also entail some degree of
   resource management control, routing control, and/or capacity
   augmentation.

   As noted previously, congestion is an undesirable phenomena in



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   operational networks. Therefore, the next subsection addresses the
   issue of congestion and its ramifications within the problem context
   of Internet traffic engineering.


2.3.1 Congestion and its Ramifications


   Congestion is one of the most significant problems in an operational
   IP context. A network element is said to be congested if it
   experiences sustained overload over an interval of time. Congestion
   almost always results in degradation of service quality to end users.
   Congestion control schemes can include demand side policies and
   supply side policies. Demand side policies may restrict access to
   congested resources and/or dynamically regulate the demand to
   alleviate the overload situation. Supply side policies may expand or
   augment network capacity to better accommodate offered traffic.
   Supply side policies may also re-allocate network resources by
   redistributing traffic over the infrastructure. Traffic
   redistribution and resource re-allocation serve to increase the
   'effective capacity' seen by the demand.

   The emphasis of this memo is primarily on congestion management
   schemes falling within the scope of the network, rather than on
   congestion management systems dependent upon sensitivity and
   adaptivity from end-systems. That is, the aspects that are considered
   in this memo with respect to congestion management are those
   solutions that can be provided by control entities operating on the
   network and by the actions of network administrators and network
   operations systems.


2.4 Solution Context


   The solution context for Internet traffic engineering involves
   analysis, evaluation of alternatives, and choice between alternative
   courses of action.  Generally the solution context is predicated on
   making reasonable inferences about the current or future state of the
   network, and subsequently making appropriate decisions that may
   involve a preference between alternative sets of action. More
   specifically, the solution context demands reasonable estimates of
   traffic workload, characterization of network state, deriving
   solutions to traffic engineering problems which may be implicitly or
   explicitly formulated, and possibly instantiating a set of control
   actions. Control actions may involve the manipulation of parameters
   associated with routing, control over tactical capacity acquisition,
   and control over the traffic management functions.

   The following list of instruments may be applicable to the solution
   context of Internet traffic engineering.

   (1) A set of policies, objectives, and requirements (which may be
       context dependent) for network performance evaluation and



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       performance  optimization.

   (2) A collection of online and possibly offline tools and mechanisms
       for measurement, characterization, modeling, and control
       of Internet traffic and control over the placement and allocation
       of network resources, as well as control over the mapping or
       distribution of traffic onto the infrastructure.

   (3) A set of constraints on the operating environment, the network
       protocols, and the traffic engineering system itself.

   (4) A set of quantitative and qualitative techniques and
       methodologies for abstracting, formulating, and
       solving traffic engineering problems.

   (5) A set of administrative control parameters which may be
       manipulated through a Configuration Management (CM) system.
       The CM system itself may include a configuration control
       subsystem, a configuration repository, a configuration
       accounting subsystem, and a configuration auditing subsystem.

   (6) A set of guidelines for network performance evaluation,
       performance optimization, and performance improvement.

   Derivation of traffic characteristics through measurement and/or
   estimation is very useful within the realm of the solution space for
   traffic engineering. Traffic estimates can be derived from customer
   subscription information, traffic projections, traffic models, and
   from actual empirical measurements. The empirical measurements may be
   performed at the traffic aggregate level or at the flow level in
   order to derive traffic statistics at various levels of detail.
   Measurements at the flow level or on small traffic aggregates may be
   performed at edge nodes, where traffic enters and leaves the network.
   Measurements at large traffic aggregate levels may be performed
   within the core of the network where potentially numerous traffic
   flows may be in transit concurrently.

   To conduct performance studies and to support planning of existing
   and future networks, a routing analysis may be performed to determine
   the path(s) the routing protocols will choose for various traffic
   demands, and to ascertain the utilization of network resources as
   traffic is routed through the network. The routing analysis should
   capture the selection of paths through the network, the assignment of
   traffic across multiple feasible routes, and the multiplexing of IP
   traffic over traffic trunks (if such constructs exists) and over the
   underlying network infrastructure. A network topology model is a
   necessity for routing analysis. A network topology model may be
   extracted from network architecture documents, from network designs,
   from information contained in router configuration files, from
   routing databases, from routing tables, or from automated tools that
   discover and depict network topology information. Topology
   information may also be derived from servers that monitor network
   state, and from servers that perform provisioning functions.




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   Routing in operational IP networks can be administratively controlled
   at various levels of abstraction including the manipulation of BGP
   attributes; manipulation of IGP metrics. For path oriented
   technologies such as MPLS and its derivatives, routing can be further
   controlled by the manipulation of relevant traffic engineering
   parameters, resource parameters, and administrative policy
   constraints.  Within the context of MPLS, the path of an explicit
   label switched path (LSP) can be computed and established in various
   ways including: (1) manually, (2) automatically online using
   constraint-based routing processes implemented on label switching
   routers, and (3) automatically offline using constraint-based routing
   entities implemented on external traffic engineering support systems.


2.4.1 Combating the Congestion Problem


   Minimizing congestion is a significant aspect of Internet traffic
   engineering.  This subsection gives an overview of the general
   approaches that have been used or proposed to combat congestion
   problems.

   Congestion management policies can be categorized based upon the
   following criteria (see e.g., [YaRe95] for a more detailed taxonomy
   of congestion control schemes): (1) Response time scale which can be
   characterized as long, medium, or short; (2) reactive versus
   preventive which relates to congestion control and congestion
   avoidance; and (3) supply side versus demand side congestion
   management schemes. These aspects are discussed in the following
   paragraphs.

   (1) Congestion Management based on Response Time Scales

   - Long (weeks to months): Capacity planning works over a relatively
   long time scale to expand network capacity based on estimates or
   forecasts of future traffic demand and traffic distribution. Since
   router and link provisioning take time and are generally expensive,
   these upgrades are typically carried out in the weeks-to-months or
   even years time scale.

   - Medium (minutes to days): Several control policies fall within the
   medium time scale category. Examples include: (1) Adjusting IGP
   and/or BGP parameters to route traffic away or towards certain
   segments of the network; (2) Setting up and/or adjusting some
   explicitly routed label switched paths (ER-LSPs) in MPLS networks to
   route some traffic trunks away from possibly congested resources or
   towards possibly more favorable routes; (3) re-configuring the
   logical topology of the network to make it correlate more closely
   with the spatial traffic distribution using for example some
   underlying path-oriented technology such as MPLS LSPs, ATM PVCs, or
   optical channel trails (see e.g. [AWD6]).  Many of these adaptive
   medium time scale response schemes rely on a measurement system that
   monitors changes in traffic distribution, traffic shifts, and network
   resource utilization and subsequently provides feedback to the online



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   and/or offline traffic engineering mechanisms and tools which employ
   this feedback information to trigger certain control actions to occur
   within the network. The traffic engineering mechanisms and tools can
   be implemented in a distributed fashion or in a centralized fashion,
   and may have a hierarchical structure or a flat structure. The
   comparative merits  of distributed and centralized control structures
   for networks are well known. A centralized scheme may have global
   visibility into the network state and may produce potentially more
   optimal solutions. However, centralized schemes are prone to single
   points of failure and may not scale as well as distributed schemes.
   Moreover, the information utilized by a centralized scheme may be
   stale and may not reflect the actual state of the network. It is not
   an objective of this memo to make a recommendation between
   distributed and centralized schemes. This is a choice that network
   administrators must make based on their specific needs.

   - Short (picoseconds to minutes): This category includes packet level
   processing functions and events on the order of several round trip
   times. It includes router mechanisms such as passive and active
   buffer management. These mechanisms are used to control congestion
   and/or signal congestion to end systems so that they can adaptively
   regulate the rate at which traffic is injected into the network. One
   of the most popular active queue management schemes, especially for
   TCP traffic, is Random Early Detection (RED) [FlJa93], which supports
   congestion avoidance by controlling the average queue size. During
   congestion (but before the queue is filled), the RED scheme chooses
   arriving packets to "mark" according to a probabilistic algorithm
   which takes into account the average queue size. For a router that
   does not utilize explicit congestion notification (ECN) see e.g.,
   [Floy94]), the marked packets can simply be dropped to signal the
   inception of congestion to end systems. On the other hand, if the
   router supports ECN, then it can set the ECN field in the packet
   header. Several variations of RED have been proposed to support
   different drop precedence levels in multiclass environments [RFC-
   2597], e.g., RED with In and Out (RIO) and Weighted RED. There is
   general consensus that RED provides congestion avoidance performance
   which is not worse than traditional Tail-Drop (TD) queue management
   (drop arriving packets only when the queue is full). Importantly,
   however, RED reduces the possibility of global synchronization and
   improves fairness among different TCP sessions. However, RED by
   itself can not prevent congestion and unfairness caused by
   unresponsive sources, e.g., UDP traffic and some misbehaved greedy
   connections. Other schemes have been proposed to improve the
   performance and fairness in the presence of unresponsive traffic.
   Some of these schemes were proposed as theoretical frameworks and are
   typically not available in existing commercial products. Two such
   schemes are Longest Queue Drop (LQD) and Dynamic Soft Partitioning
   with Random Drop (RND) [SLDC98].

   (2) Congestion Management: Reactive versus Preventive Schemes

   - Reactive: reactive (recovery) congestion management policies react
   to existing congestion problems to improve it. All the policies
   described in the long and medium time scales above can be categorized



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   as being reactive especially if the policies are based on monitoring
   and identifying existing congestion problems, and on the initiation
   of relevant actions to ease the situation.

   - Preventive: preventive (predictive/avoidance) policies take
   proactive action to prevent congestion based on estimates and
   predictions of future potential congestion problems. Some of the
   policies described in the long and medium time scales fall into this
   category. They do not necessarily respond immediately to existing
   congestion problems. Instead forecasts of traffic demand and workload
   distribution are considered and action may be taken to prevent
   potential congestion problems in the future. The schemes described in
   the short time scale (e.g., RED and its variations, ECN, LQD, and
   RND) are also used for congestion avoidance since dropping or marking
   packets before queues actually overflow would trigger corresponding
   TCP sources to slow down.

   (3) Congestion Management: Supply Side versus Demand Side Schemes

   - Supply side: supply side congestion management policies increase
   the effective capacity available to traffic in order to control or
   obviate congestion. This can be accomplished by augmenting capacity.
   Another way to accomplish this is to minimize congestion by having a
   relatively balanced distribution of traffic over the network. For
   example, capacity planning should aim to provide a physical topology
   and associated link bandwidths that match estimated traffic workload
   and traffic distribution based on forecasting (subject to budgetary
   and other constraints).  However, if actual traffic distribution does
   not match the topology derived from capacity panning (due to
   forecasting errors or facility constraints for example), then the
   traffic can be mapped onto the existing topology using routing
   control mechanisms, using path oriented technologies (e.g., MPLS LSPs
   and optical channel trails) to modify the logical topology, or by
   using some other load redistribution mechanisms.

   - Demand side: demand side congestion management policies control or
   regulate the offered traffic to alleviate congestion problems. For
   example, some of the short time scale mechanisms described earlier
   (such as RED and its variations, ECN, LQD, and RND) as well as
   policing and rate shaping mechanisms attempt to regulate the offered
   load in various ways. Tariffs may also be applied as a demand side
   instrument. To date, however, tariffs have not been used as a means
   of demand side congestion management within the Internet.

   In summary, a variety of mechanisms can be used to address congestion
   problems in IP networks. These mechanisms may operate at multiple
   time-scales.


2.5 Implementation and Operational Context


   The operational context of Internet traffic engineering is
   characterized by constant change which occur at multiple levels of



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   abstraction.  The implementation context demands effective planning,
   organization, and execution. The planning aspects may involve
   determining prior sets of actions to achieve desired objectives.
   Organizing involves arranging and assigning responsibility to the
   various components of the traffic engineering system and coordinating
   the activities to accomplish the desired TE objectives. Execution
   involves measuring and applying corrective or perfective actions to
   attain and maintain desired TE goals.


3.0 Traffic Engineering Process Model(s)


   This section describes a generic process model that captures the high
   level practical aspects of Internet traffic engineering in an
   operational context. The process model is described as a sequence of
   actions that a traffic engineer, or more generally a traffic
   engineering system, must perform to optimize the performance of an
   operational network (see also [AWD1, AWD2]). The process model
   described here represents the broad activities common to most traffic
   engineering methodologies although the details regarding how traffic
   engineering is executed may differ from network to network. This
   process model may be enacted explicitly or implicitly, by an
   automaton and/or by a human.

   The traffic engineering process model is iterative [AWD2]. The four
   phases of the process model described below are repeated continually.

   The first phase of the TE process model is to define the relevant
   control policies that govern the operation of the network. These
   policies may depend upon many factors including the prevailing
   business model, the network cost structure, the operating
   constraints, the utility model, and optimization criteria.

   The second phase of the process model is a feedback mechanism
   involving the acquisition of measurement data from the operational
   network. If empirical data is not readily available from the network,
   then synthetic workloads may be used instead which reflect either the
   prevailing or the expected workload of the network. Synthetic
   workloads may be derived by estimation or extrapolation using prior
   empirical data.  Their derivation may also be obtained using
   mathematical models of traffic characteristics or other means.

   The third phase of the process model is to analyze the network state
   and to characterize traffic workload. Performance analysis may be
   proactive and/or reactive. Proactive performance analysis identifies
   potential problems that do not exist, but could manifest in the
   future. Reactive performance analysis identifies existing problems,
   determines their cause through diagnosis, and evaluates alternative
   approaches to remedy the problem, if necessary. A number of
   quantitative and qualitative techniques may be used in the analysis
   process, including modeling based analysis and simulation. The
   analysis phase of the process model may involve investigating the
   concentration and distribution of traffic across the network or



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   relevant subsets of the network, identifying the characteristics of
   the offered traffic workload, identifying existing or potential
   bottlenecks, and identifying network pathologies such as ineffective
   link placement, single points of failures, etc. Network pathologies
   may result from many factors including inferior network architecture,
   inferior network design, and configuration problems.  A traffic
   matrix may be constructed as part of the analysis process. Network
   analysis may also be descriptive or prescriptive.

   The fourth phase of the TE process model is the performance
   optimization of the network. The performance optimization phase
   involves a decision process which selects and implements a set of
   actions from a set of alternatives.  Optimization actions may include
   the use of appropriate techniques to either control the offered
   traffic or to control the distribution of traffic across the network.
   Optimization actions may also involve adding additional links or
   increasing link capacity, deploying additional hardware such as
   routers and switches, systematically adjusting parameters associated
   with routing such as IGP metrics and BGP attributes, and adjusting
   traffic management parameters. Network performance optimization may
   also involve starting a network planning process to improve the
   network architecture, network design, network capacity, network
   technology, and the configuration of network elements to accommodate
   current and future growth.


3.1 Components of the Traffic Engineering Process Model


   The key components of the traffic engineering process model include a
   measurement subsystem, a modeling and analysis subsystem, and an
   optimization subsystem. The following subsections examine these
   components as they apply to the traffic engineering process model.


3.2 Measurement


   Measurement is crucial to the traffic engineering function. The
   operational state of a network can be conclusively determined only
   through measurement. Measurement is also critical to the optimization
   function because it provides feedback data which is used by traffic
   engineering control subsystems.  This data is used to adaptively
   optimize network performance in response to events and stimuli
   originating within and outside the network. Measurement is also
   needed to determine the quality of network services and to evaluate
   the effectiveness of traffic engineering policies. Experience
   suggests that measurement is most effective when acquired and applied
   systematically.

   When developing a measurement system to support the traffic
   engineering function in IP networks, the following questions should
   be carefully considered: Why is measurement needed in this particular
   context? What parameters are to be measured?  How should the



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   measurement be accomplished?  Where should the measurement be
   performed? When should the measurement be performed?  How frequently
   should the monitored variables be measured?  What level of
   measurement accuracy and reliability is desirable? What level of
   measurement accuracy and reliability is realistically attainable? To
   what extent can the measurement system permissibly interfere with the
   monitored network components and variables? What is the acceptable
   cost of measurement? The answers to these questions will determine
   the measurement tools and methodologies appropriate in any given
   traffic engineering context.

   It should also be noted that there is a distinction between
   measurement and evaluation. Measurement provides raw data concerning
   state parameters and variables of monitored network elements.
   Evaluation utilizes the raw data to make inferences regarding the
   monitored system.

   Measurement in support of the TE function can occur at different
   levels of abstraction. For example, measurement can be used to derive
   packet level characteristics, flow level characteristics, user or
   customer level characteristics, traffic aggregate characteristics,
   component level characteristics, network wide characteristics, etc.


3.3 Modeling, Analysis, and Simulation


   Modeling and analysis are important aspects of Internet traffic
   engineering. Modeling involves constructing an abstract or physical
   representation which depicts relevant traffic characteristics and
   network attributes.

   A network model is an abstract representation of the network which
   captures relevant network features, attributes, and characteristics,
   such as link and nodal attributes and constraints.  A network model
   may facilitate analysis and/or simulation which can be used to
   predict network performance under various conditions as well as to
   guide network expansion plans.

   In general, Internet traffic engineering models can be classified as
   either structural or behavioral. Structural models focus on the
   organization of the network and its components. Behavioral models
   focus on the dynamics of the network and the traffic workload.
   Modeling for Internet traffic engineering may also be formal or
   informal.

   Accurate behavioral models for traffic sources are particularly
   useful for analysis. Development of behavioral traffic source models
   that are consistent with empirical data obtained from operational
   networks is a major research topic in Internet traffic engineering.
   These source models should also be tractable and amenable to
   analysis. The topic of source models for IP traffic is a research
   topic and is therefore outside the scope of this document.  Its
   importance, however, must be emphasized.



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   Network simulation tools are extremely useful for traffic
   engineering. Because of the complexity of realistic quantitative
   analysis of network behavior, certain aspects of network performance
   studies can only be conducted effectively using simulation.  A good
   network simulator can be used to mimic and visualize network
   characteristics under various conditions in a safe and non-disruptive
   manner.  For example, a network simulator may be used to depict
   congested resources and hot spots, and to provide hints regarding
   possible solutions to network performance problems. A good simulator
   may also be used to validate the effectiveness of planned solutions
   to network issues without the need to tamper with the operational
   network, or to commence an expensive network upgrade which may not
   achieve the desired objectives. Furthermore, during the process of
   network planning, a network simulator may reveal pathologies such as
   single points of failure which may require additional redundancy, and
   potential bottlenecks and hot spots which may require additional
   capacity.

   Routing simulators are especially useful in large networks. A routing
   simulator may identify planned links which may not actually be used
   to route traffic by the existing routing protocols. Simulators can
   also be used to conduct scenario based and perturbation based
   analysis, as well as sensitivity studies.  Simulation results can be
   used to initiate appropriate actions in various ways. For example, an
   important application of network simulation tools is to investigate
   and identify how best to evolve and grow the network in order to
   accommodate projected future demands.


3.4 Optimization


   Network performance optimization involves resolving network issues by
   transforming such issues into concepts that enable a solution,
   identification of a solution, and implementation of the solution.
   Network performance optimization can be corrective or perfective. In
   corrective optimization, the goal is to remedy a problem that has
   occurred or that is incipient. In perfective optimization, the goal
   is to improve network performance even when explicit problems do not
   exist and are not anticipated.

   Network performance optimization is a continual process, as noted
   previously.  Performance optimization iterations may consist of
   real-time optimization sub-processes and non-real-time network
   planning sub-processes.  The difference between real-time
   optimization and network planning is primarily in the relative time-
   scale in they operate and in the granularity of actions.  One of the
   objectives of a real-time optimization sub-process is to control the
   mapping and distribution of traffic over the existing network
   infrastructure to avoid and/or relieve congestion, to assure
   satisfactory service delivery, and to optimize resource utilization.
   Real-time optimization is needed because random incidents such as
   fiber cuts or shifts in traffic demand will occur irrespective of how
   well a network is designed. These incidents can cause congestion and



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   other problems to manifest in an operational network.  Real-time
   optimization must solve such problems in small to medium time-scales
   ranging from micro-seconds to minutes or hours. Examples of real-time
   optimization include queue management, IGP/BGP metric tuning, and
   using technologies such as MPLS explicit LSPs to change the paths of
   some traffic trunks [XIAO].

   One of the functions of the network planning sub-process is to
   initiate actions to systematically evolve the architecture,
   technology, topology, and capacity of a network. When a problem
   exists in the network, real-time optimization should provide an
   immediate remedy. Because a prompt response is necessary, the real-
   time solution may not be the best possible solution.  Network
   planning may subsequently be needed to refine the solution and
   improve the situation.  Network planning is also required to expand
   the network to support traffic growth and changes in traffic
   distribution over time. As previously noted, a change in the topology
   and/or capacity of the network may be the outcome of network
   planning.

   Clearly, network planning and real-time performance optimization are
   mutually complementary activities. A well-planned and designed
   network makes real-time optimization easier, while a systematic
   approach to real-time network performance optimization allows network
   planning to focus on long term issues rather than tactical
   considerations.  Systematic real-time network performance
   optimization also provides valuable inputs and insights toward
   network planning.

   Stability is an important consideration in real-time network
   performance optimization. This aspect will be repeatedly addressed
   throughout this memo.


4.0 Historical Review and Recent Developments


   This section briefly reviews different traffic engineering approaches
   proposed and implemented in telecommunications and computer networks.
   The discussion is not intended to be comprehensive.  It is primarily
   intended to illuminate pre-existing perspectives and prior art
   concerning traffic engineering in the Internet and in legacy
   telecommunications networks.


4.1 Traffic Engineering in Classical Telephone Networks


   This subsection presents a brief overview of traffic engineering in
   telephone networks which often relates to the way user traffic is
   steered from an originating node to the terminating node.  This
   subsection presents a brief overview of this topic. A detailed
   description of the various routing strategies applied in telephone
   networks is included in the book by G. Ash [ASH2].



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   The early telephone network relied on static hierarchical routing,
   whereby routing patterns remained fixed independent of the state of
   the network or time of day. The hierarchy was intended to accommodate
   overflow traffic, improve network reliability via alternate routes,
   and prevent call looping by employing strict hierarchical rules.  The
   network was typically over-provisioned since a given fixed route had
   to be dimensioned so that it could carry user traffic during a busy
   hour of any busy day.  Hierarchical routing in the telephony network
   was found to be too rigid upon the advent of digital switches and
   stored program control which were able to manage more complicated
   traffic engineering rules.

   Dynamic routing was introduced to alleviate the routing inflexibility
   in the static hierarchical routing so that the network would operate
   more efficiently. This resulted in significant economic gains
   [HuSS87].  Dynamic routing typically reduces the overall loss
   probability by 10 to 20 percent (compared to static hierarchical
   routing).  Dynamic routing can also improve network resilience by
   recalculating routes on a per-call basis and periodically updating
   routes.

   There are three main types of dynamic routing in the telephone
   network. They are time-dependent routing, state-dependent routing
   (SDR), and event dependent routing (EDR).

   In time-dependent routing, regular variations in traffic loads due to
   time of day and seasonality are exploited in pre-planned routing
   tables.  In state-dependent routing, routing tables are updated
   online according to the current state of the network (e.g, traffic
   demand, utilization, etc.).  In event dependent routing, routing
   changes are incepted by events (such as call setups encountering
   congested or blocked links) whereupon new paths are searched out
   using learning models.  EDR methods are real-time adaptive, but they
   do not require global state information as does SDR.  Examples of EDR
   schemes include the dynamic alternate routing (DAR) from BT, the
   state-and-time dependent routing (STR) from NTT, and the success-to-
   the-top (STT) routing from AT&T.

   Dynamic non-hierarchical routing (DNHR) is an example of dynamic
   routing that was introduced in the AT&T toll network in the 1980's to
   respond to time-dependent information such as regular load variations
   as a function of time.  Time-dependent information in terms of load
   may be divided into three time scales: hourly, weekly, and yearly.
   Correspondingly, three algorithms are defined to pre-plan the routing
   tables.  The network design algorithm operates over a year-long
   interval while the demand servicing algorithm operates on a weekly
   basis to fine tune link sizes and routing tables to correct forecast
   errors on the yearly basis. At the smallest time scale, the routing
   algorithm is used to make limited adjustments based on daily traffic
   variations.  Network design and demand servicing are computed using
   offline calculations.  Typically, the calculations require extensive
   search on possible routes.  On the other hand, routing may need
   online calculations to handle crankback.  DNHR adopts a "two-link"
   approach whereby a path can consist of two links at most.  The



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   routing algorithm presents an ordered list of route choices between
   an originating switch and a terminating switch.  If a call overflows,
   a via switch (a tandem exchange between the originating switch and
   the terminating switch) would send a crankback signal to the
   originating switch.  This switch would then select the next route,
   and so on, until there are no alternative routes available in which
   the call is blocked.


4.2 Evolution of Traffic Engineering in Packet Networks


   This subsection reviews related prior work that was intended to
   improve the performance of data networks.  Indeed, optimization of
   the performance of data networks started in the early days of the
   ARPANET. Other early commercial networks such as SNA also recognized
   the importance of performance optimization and service
   differentiation.

   In terms of traffic management, the Internet has been a best effort
   service environment until recently. In particular, very limited
   traffic management capabilities existed in IP networks to provide
   differentiated queue management and scheduling services to packets
   belonging to different classes.

   In terms of routing control, the Internet has employed distributed
   protocols for intra-domain routing. These protocols are highly
   scalable and resilient. However, they are based on simple algorithms
   for path selection which have very limited functionality to allow
   flexible control of the path selection process.

   In the following subsections, the evolution of practical traffic
   engineering mechanisms in IP networks and its predecessors is
   reviewed.


4.2.1 Adaptive Routing in the ARPANET


   The early ARPANET recognized the importance of adaptive routing where
   routing decisions were based on the current state of the network
   [McQ80].  Early minimum delay routing approaches forwarded each
   packet to its destination along a path for which the total estimated
   transit time is the smallest.  Each node maintained a table of
   network delays, representing the estimated delay that a packet would
   experience along a given path toward its destination. The minimum
   delay table was periodically transmitted by a node to its neighbors.
   The shortest path, in terms of hop count, was also propagated to give
   the connectivity information.

   One drawback to this approach is that dynamic link metrics tend to
   create "traffic magnets" causing congestion to be shifted from one
   location of a network to another location, resulting in oscillation
   and network instability.



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4.2.2 Dynamic Routing in the Internet


   The Internet evolved from the APARNET and adopted dynamic routing
   algorithms with distributed control to determine the paths that
   packets should take en-route to their destinations.  The routing
   algorithms are adaptations of shortest path algorithms where costs
   are based on link metrics. The link metric can be based on static or
   dynamic quantities. The link metric based on static quantities may be
   assigned administratively according to local criteria. The link
   metric based on dynamic quantities may be a function of a network
   congestion measure such as delay or packet loss.

   It was apparent early that static link metric assignment was
   inadequate because it can easily lead to unfavorable scenarios in
   which some links become congested while others remain lightly loaded.
   One of the many reasons for the inadequacy of static link metrics is
   that link metric assignment was often done without considering the
   traffic matrix in the network.  Also, the routing protocols did not
   take traffic attributes and capacity constraints into account when
   making routing decisions. This results in traffic concentration being
   localized in subsets of the network infrastructure and potentially
   causing congestion.  Even if link metrics are assigned in accordance
   with the traffic matrix, unbalanced loads in the network can still
   occur due to a number factors including:

    - Resources may not be deployed in the most optimal locations
      from a routing perspective.

    - Forecasting errors in traffic volume and/or traffic distribution.

    - Dynamics in traffic matrix due to the temporal nature of traffic
      patterns, BGP policy change from peers, etc.

   The inadequacy of the legacy Internet interior gateway routing system
   is one of the factors motivating the interest in path oriented
   technologies with explicit routing and constraint-based routing
   capability, such as MPLS.


4.2.3 ToS Routing


   Type-of-Service (ToS) routing involves different routes going to the
   same destination being selected depending upon the ToS field of an IP
   packet [RFC-1349].  The ToS classes may be classified as low delay
   and high throughput.  Each link is associated with multiple link
   costs and each link cost is used to compute routes for a particular
   ToS.  A separate shortest path tree is computed for each ToS. The
   shortest path algorithm must be run for each ToS resulting in very
   expensive computation.  Classical ToS-based routing is now outdated
   as the IP header field has been replaced by a Diffserv field.
   Effective traffic engineering is difficult to perform in classical
   ToS-based routing because each class still relies exclusively on



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   shortest path routing which results in localization of traffic
   concentration within the network.


4.2.4 Equal Cost MultiPath


   Equal Cost MultiPath (ECMP) is another technique that attempts to
   address the deficiency in Shortest Path First (SPF) interior gateway
   routing systems [RFC-2178]. In the classical SPF algorithm, if two or
   more shortest paths exist to a given destination, the algorithm will
   choose one of them.  The algorithm is modified slightly in ECMP so
   that if two or more equal cost shortest paths exist between two
   nodes, the traffic between the nodes is distributed among the
   multiple equal-cost paths.  Traffic distribution across the equal-
   cost paths is usually performed in one of two ways: (1) packet-based
   in a round-robin fashion, or (2) flow-based using hashing on source
   and destination IP addresses and possibly other fields of the IP
   header.  The first approach can easily cause out-of-order packets
   while the second approach is dependent upon the number and
   distribution of flows.  Flow-based load sharing may be unpredictable
   in an enterprise network where the number of flows is relatively
   small and less heterogeneous (for example, hashing may not be
   uniform), but it is generally effective in core public networks where
   the number of flows is large and heterogeneous.

   In ECMP, link costs are static and bandwidth constraints are not
   considered, so ECMP attempts to distribute the traffic as equally as
   possible among the equal-cost paths independent of the congestion
   status of each path.  As a result, given two equal-cost paths, it is
   possible that one of the paths will be more congested than the other.
   Another drawback of ECMP is that load sharing cannot be achieved on
   multiple paths which have non-identical costs.


4.2.5 Nimrod


   Nimrod is a routing system developed to provide heterogeneous service
   specific routing in the Internet, while taking multiple constraints
   into account [RFC-1992].  Essentially, Nimrod is a link state routing
   protocol which supports path oriented packet forwarding. It uses the
   concept of maps to represent network connectivity and services at
   multiple levels of abstraction. Mechanisms are provided to allow
   restriction of the distribution of routing information.

   Even though Nimrod did not enjoy deployment in the public Internet, a
   number of key concepts incorporated into the Nimrod architecture,
   such as explicit routing which allows selection of paths at
   originating nodes, are beginning to find applications in some recent
   constraint-based routing initiatives.






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4.3 Overlay Model


   In the overlay model, a virtual-circuit network, such as ATM, frame
   relay, or WDM provides virtual-circuit connectivity between routers
   that are located at the edges of a virtual-circuit cloud.  In this
   mode, two routers that are connected through a virtual circuit see a
   direct adjacency between themselves independent of the physical route
   taken by the virtual circuit through the ATM, frame relay, or WDM
   network.  Thus, the overlay model essentially decouples the logical
   topology that routers see from the physical topology that the ATM,
   frame relay, or WDM network manages.  The overlay model based on ATM
   or frame relay enables a network administrator or an automaton to
   employ traffic engineering concepts to perform path optimization by
   re-configuring or rearranging the virtual circuits so that a virtual
   circuit on a congested or suboptimal physical link can be re-routed
   to a less congested or more optimal one. In the overlay model,
   traffic engineering is also employed to establish relationships
   between the traffic management parameters (e.g. PCR, SCR, MBS for
   ATM; or CIR, Be, and Bc for frame relay) of the virtual-circuit
   technology and the actual traffic that traverses each circuit. These
   relationships can be established based upon known or projected
   traffic profiles, and some other factors.

   The overlay model using IP over ATM requires the management of two
   separate networks with different technologies (IP and ATM) resulting
   in increased operational complexity and cost.  In the fully-meshed
   overlay model, each router would peer to every other router in the
   network, so that the total number of adjacencies is a quadratic
   function of the number of routers. Some of the issues with the
   overlay model are discussed in [AWD2].


4.4 Constrained-Based Routing


   Constraint-based routing refers to a class of routing systems that
   compute routes through a network subject to satisfaction of a set of
   constraints and requirements. In the most general setting,
   constraint-based routing may also seek to optimize overall network
   performance while minimizing costs.

   The constraints and requirements may be imposed by the network itself
   or by administrative policies. Constraints may include bandwidth, hop
   count, delay, and policy instruments such as resource class
   attributes. Constraints may also include domain specific attributes
   of certain network technologies and contexts which impose
   restrictions on the solution space of the routing function. Path
   oriented technologies such as MPLS have made constraint-based routing
   feasible and attractive in public IP networks.

   The concept of constraint-based routing within the context of MPLS
   traffic engineering requirements in IP networks was first defined in
   [AWD1].



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   Unlike QoS routing (see [RFC-2386] and the references therein) which
   generally addresses the issue of routing individual traffic flows to
   satisfy prescribed flow based QoS requirements subject to network
   resource availability, constraint-based routing is applicable to
   traffic aggregates as well as flows and may be subject to a wide
   variety of constraints which may include policy restrictions.



4.5 Overview of Other IETF Projects Related to Traffic Engineering


   This subsection reviews a number of IETF activities pertinent to
   Internet traffic engineering. These activities are primarily intended
   to evolve the IP architecture to support new service definitions
   which allow preferential or differentiated treatment to be accorded
   to certain types of traffic.


4.5.1 Integrated Services


   The IETF Integrated Services working group developed the integrated
   services (Intserv) model.  This model requires resources, such as
   bandwidth and buffers, to be reserved a priori for a given traffic
   flow to ensure that the quality of service requested by the traffic
   flow is satisfied. The integrated services model includes additional
   components beyond those used in the best-effort model such as packet
   classifiers, packet schedulers, and admission control.  A packet
   classifier is used to identify flows that are to receive a certain
   level of service. A packet scheduler handles the scheduling of
   service to different packet flows to ensure that QoS commitments are
   met.  Admission control is used to determine whether a router has the
   necessary resources to accept a new flow.

   Two services have been defined under the Integrated Services model:
   guaranteed service [RFC-2212] and controlled-load service [RFC-2211].

   The guaranteed service can be used for applications requiring bounded
   packet delivery time. For this type of application, data that is
   delivered to the application after a pre-defined amount of time has
   elapsed is usually considered worthless. Therefore, guaranteed
   service was intended to provide a firm quantitative bound on the
   end-to-end packet delay for a flow. This is accomplished by
   controlling the queuing delay on network elements along the data flow
   path. The guaranteed service model does not, however, provide  bounds
   on jitter (inter-arrival times between consecutive packets).

   The controlled-load service can be used for adaptive applications
   that can tolerate some delay but are sensitive to traffic overload
   conditions. This type of application typically functions
   satisfactorily when the network is lightly loaded but its performance
   degrades significantly when the network is heavily loaded.
   Controlled-load service therefore has been designed to provide



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   approximately the same service as best-effort service in a lightly
   loaded network regardless of actual network conditions.  Controlled-
   load service is described qualitatively in that no target values of
   delay or loss are specified.

   The main issue with the Integrated services model has been
   scalability, especially in large public IP networks which may
   potentially have millions of active micro-flows in transit
   concurrently.

   A notable feature of the Integrated Services model is that it
   requires explicit signaling of QoS requirements from end systems to
   routers [RFC-2753]. The Resource Reservation Protocol (RSVP) performs
   this signaling function and is a critical component of the Integrated
   Services model. The RSVP protocol is described next.


4.5.2 RSVP


   RSVP is a soft state signaling protocol [RFC-2205].  It supports
   receiver initiated establishment of resource reservations for both
   multicast and unicast flows. RSVP was originally developed as a
   signaling protocol within the integrated services framework for
   applications to communicate QoS requirements to the network and for
   the network to reserve relevant resources to satisfy the QoS
   requirements [RFC-2205].

   Under RSVP, the sender or source node sends a PATH message to the
   receiver with the same source and destination addresses as the
   traffic which the sender will generate. The PATH message contains:
   (1) a sender Tspec specifying the characteristics of the traffic, (2)
   a sender Template specifying the format of the traffic, and (3) an
   optional Adspec which is used to support the concept of one pass with
   advertising" (OPWA) [RFC-2205].  Every intermediate router along the
   path forwards the PATH Message to the next hop determined by the
   routing protocol. Upon receiving a PATH Message, the receiver
   responds with a RESV message which includes a flow descriptor used to
   request resource reservations. The RESV message travels to the sender
   or source node in the opposite direction along the path that the PATH
   message traversed. Every intermediate router along the path can
   reject or accept the reservation request of the RESV message.  If the
   request is rejected, the rejecting router will send an error message
   to the receiver and the signaling process will terminate. If the
   request is accepted, link bandwidth and buffer space are allocated
   for the flow and the related flow state information is installed in
   the router.

   One of the issues with the original RSVP specification was
   scalability. This is because reservations were required for micro-
   flows, so that the amount of state maintained by network elements
   tends to increase linearly with the number of micro-flows.

   Recently, RSVP has been modified and extended in several ways to



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   overcome the scaling problems. As a result, it is becoming a
   versatile signaling protocol for the Internet. For example, RSVP has
   been extended to reserve resources for aggregation of flows, to set
   up MPLS explicit label switched paths, and to perform other signaling
   functions within the Internet. There are also a number of proposals
   to reduce the amount of refresh messages required to maintain
   established RSVP sessions [Berger].

   A number of IETF working groups have been engaged in activities
   related to the RSVP protocol. These include the original RSVP working
   group, the MPLS working group, the Resource Allocation Protocol
   working group, and the Policy Framework working group.



4.5.3 Differentiated Services


   The goal of the Differentiated Services (Diffserv) effort within the
   IETF is to devise scalable mechanisms for categorization of traffic
   into behavior aggregates, which ultimately allows each behavior
   aggregate to be treated differently, especially when there is a
   shortage of resources such as link bandwidth and buffer space [RFC-
   2475]. One of the primary motivations for the Diffserv effort was to
   devise alternative mechanisms for service differentiation in the
   Internet that mitigate the scalability issues encountered with the
   Intserv model.

   The IETF Diffserv working group has defined a Differentiated Services
   field in the IP header (DS field). The DS field consists of six bits
   of the part of the IP header formerly known as TOS octet. The DS
   field is used to indicate the forwarding treatment that a packet
   should receive at a node [RFC-2474]. The Diffserv working group has
   also standardized a number of Per-Hop Behavior (PHB) groups. Using
   the PHBs, several classes of services can be defined using different
   classification, policing, shaping and scheduling rules.

   For an end-user of network services to receive Differentiated
   Services from its Internet Service Provider (ISP), it may be
   necessary for the user to have a Service Level Agreement (SLA) with
   the ISP. An SLA may explicitly or implicitly specify a Traffic
   Conditioning Agreement (TCA) which defines classifier rules as well
   as metering, marking, discarding, and shaping rules.

   Packets are classified, and possibly policed and shaped at the
   ingress to a Diffserv network. When a packet traverses the boundary
   between different Diffserv domains, the DS field of the packet may be
   re-marked according to existing agreements between the domains.

   Differentiated Services allows only a finite number of service
   classes to be indicated by the DS field. The main advantage of the
   Diffserv approach relative to the Intserv model is scalability.
   Resources are allocated on a per-class basis and the amount of state
   information is proportional to the number of classes rather than to



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   the number of application flows.

   It should be obvious from the previous discussion that the Diffserv
   model essentially deals with traffic management issues on a per hop
   basis. The Diffserv control model consists of a collection of micro-
   TE control mechanisms. Other traffic engineering capabilities, such
   as capacity management (including routing control), are also required
   in order to deliver acceptable service quality in Diffserv networks.


4.5.4 MPLS


   MPLS is an advanced forwarding scheme which also includes extensions
   to conventional IP control plane protocols. MPLS extends the Internet
   routing model and enhances packet forwarding and path control [RoVC].

   At the ingress to an MPLS domain, label switching routers (LSRs)
   classify IP packets into forwarding equivalence classes (FECs) based
   on a variety of factors, including e.g. a combination of the
   information carried in the IP header of the packets and the local
   routing information maintained by the LSRs. An MPLS label is then
   appended to each packet according to their forwarding equivalence
   classes. In a non-ATM/FR environment, the label is 32 bits long and
   contains a 20-bit label field, a 3-bit experimental field (formerly
   known as Class-of-Service or CoS field), a 1-bit label stack
   indicator and an 8-bit TTL field. In an ATM (FR) environment, the
   label consists information encoded in the VCI/VPI (DLCI) field.  An
   MPLS capable router (an LSR) examines the label and possibly the
   experimental field and uses this information to make packet
   forwarding decisions.


   An LSR makes forwarding decisions by using the label prepended to
   packets as the index into a local next hop label forwarding entry
   (NHLFE). The packet is then processed as specified in the NHLFE. The
   incoming label may be replaced by an outgoing label, and the packet
   may be switched to the next LSR. This label-switching process is very
   similar to the label (VCI/VPI) swapping process in ATM networks.
   Before a packet leaves an MPLS domain, its MPLS label may be removed.
   A Label Switched Path (LSP) is the path between an ingress LSRs and
   an egress LSRs through which a labeled packet traverses.  The path of
   an explicit LSP is defined at the originating (ingress) node of the
   LSP. MPLS can use a signaling protocol such as RSVP or LDP to set up
   LSPs.

   MPLS is a very powerful technology for Internet traffic engineering
   because it supports explicit LSPs which allow constraint-based
   routing to be implemented efficiently in IP networks [AWD2]. The
   requirements for traffic engineering over MPLS are described in
   [AWD1]. Extensions to RSVP to support instantiation of explicit LSP
   are discussed in [AWD3]. Extensions to LDP, known as CR-LDP, to
   support explicit LSPs are presented in [JAM].




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4.5.5 IP Performance Metrics


   The IETF IP Performance Metrics (IPPM) working group has been
   developing a set of standard metrics that can be used to monitor the
   quality, performance, and reliability of Internet services. These
   metrics can be applied by network operators, end-users, and
   independent testing groups to provide users and service providers
   with a common understanding of the performance and reliability of the
   Internet component 'clouds' they use/provide [RFC2330].  The criteria
   for performance metrics developed by the IPPM WG are described in
   [RFC2330]. Examples of performance metrics include one-way packet
   loss [RFC2680], one-way delay [RFC2679], and connectivity measures
   between two nodes [RFC2678]. Other metrics include second-order
   measures of packet loss and delay.

   Some of the performance metrics specified by the IPPM WG are useful
   for specifying Service Level Agreements (SLAs).  SLAs are sets of
   service level objectives negotiated between users and service
   providers, wherein each objective is a combination of one or more
   performance metrics possibly subject to certain constraints.


4.5.6 Flow Measurement


   The IETF Real Time Flow Measurement (RTFM) working group has produced
   an architecture document defining a method to specify traffic flows
   as well as a number of components for flow measurement (meters, meter
   readers, manager) [RFC-2722]. A flow measurement system enables
   network traffic flows to be measured and analyzed at the flow level
   for a variety of purposes.  As noted in RFC-2722, a flow measurement
   system can be very useful in the following contexts: (1)
   understanding the behavior of existing networks, (2) planning for
   network development and expansion, (3) quantification of network
   performance, (4) verifying the quality of network service, and (5)
   attribution of network usage to users [RFC-2722].

   A flow measurement system consists of meters, meter readers, and
   managers. A meter observe packets passing through a measurement
   point, classifies them into certain groups, accumulates certain usage
   data (such as the number of packets and bytes for each group), and
   stores the usage data in a flow table. A group may represent a user
   application, a host, a network, a group of networks, etc.  A meter
   reader gathers usage data from various meters so it can be made
   available for analysis.  A manager is responsible for configuring and
   controlling meters and meter readers.  The instructions received by a
   meter from a manager include flow specification, meter control
   parameters, and sampling techniques.  The instructions received by a
   meter reader from a manager include the address of the meter whose
   date is to be collected, the frequency of data collection, and the
   types of flows to be collected.





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4.5.7 Endpoint Congestion Management


   The IETF Endpoint Congestion Management working group is intended to
   provide a set of congestion control mechanisms that transport
   protocols can use.  It is also intended to develop mechanisms for
   unifying congestion control across a subset of an endpoint's active
   unicast connections (called a congestion group).  A congestion
   manager continuously monitors the state of the path for each
   congestion group under its control.  The manager uses that
   information to instruct a scheduler on how to partition bandwidth
   among the connections of that congestion group.


4.6 Overview of ITU Activities Related to Traffic Engineering


   This section provides an overview of prior work within the ITU-T
   pertaining to traffic engineering in traditional telecommunications
   networks.

   ITU-T Recommendations E.600 [itu-e600], E.701 [itu-e701], and E.801
   [itu-e801] address traffic engineering issues in traditional
   telecommunications networks. Recommendation E.600 provides a
   vocabulary for describing traffic engineering concepts, while E.701
   defines reference connections, Grade of Service (GOS), and traffic
   parameters for ISDN.  Recommendation E.701 uses the concept of a
   reference connection to identify representative cases of different
   types of connections without describing the specifics of their actual
   realizations by different physical means. As defined in
   Recommendation E.600, "a connection is an association of resources
   providing means for communication between two or more devices in, or
   attached to, a telecommunication network."  Also, E.600 defines "a
   resource as any set of physically or conceptually identifiable
   entities within a telecommunication network, the use of which can be
   unambiguously determined" [itu-e600].  There can be different types
   of connections as the number and types of resources in a connection
   may vary.

   Typically, different network segments are involved in the path of a
   connection.  For example, a connection may be local, national, or
   international.  The purposes of reference connections are to clarify
   and specify traffic performance issues at various interfaces between
   different network domains.  Each domain may consist of one or more
   service provider networks.

   Reference connections provide a basis to define grade of service
   (GoS) parameters related to traffic engineering within the ITU-T
   framework.  As defined in E.600, "GoS refers to a number of traffic
   engineering variables which are used to provide a measure of the
   adequacy of a group of resources under specified conditions."  These
   GoS variables may be probability of loss, dial tone, delay, etc.
   They are essential for network internal design and operation as well
   as for component performance specification.



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   GoS is different from quality of service (QoS) in the ITU framework.
   QoS is the performance perceivable by a telecommunication service
   user and expresses the user's degree of satisfaction of the service.
   QoS parameters focus on performance aspects observable at the service
   access points and network interfaces, rather than their causes within
   the network. GoS, on the other hand, is a set of network oriented
   measures which characterize the adequacy of a group of resources
   under specified conditions.  For a network to be effective in serving
   its users, the values of both GoS and QoS parameters must be related,
   with GoS parameters typically making a major contribution to the QoS.

   Recommendation E.600 stipulates that a set of GoS parameters must be
   selected and defined on an end-to-end basis for each major service
   category provided by a network to assist the network provider improve
   efficiency and effectiveness of the network.  Based on a selected set
   of reference connections, suitable target values are assigned to the
   selected GoS parameters under normal and high load conditions.  These
   end-to-end GoS target values are then apportioned to individual
   resource components of the reference connections for dimensioning
   purposes.


5.0 Taxonomy of Traffic Engineering Systems


   This section presents a short taxonomy of traffic engineering
   systems. A taxonomy of traffic engineering systems can be constructed
   based on traffic engineering styles and views as listed below:

    - Time-dependent vs State-dependent vs Event-dependent
    - Offline vs Online
    - Centralized vs Distributed
    - Local vs Global Information
    - Prescriptive vs Descriptive
    - Open Loop vs Closed Loop
    - Tactical vs Strategic

   These classification systems are described in greater detail in the
   following subsections of this document.


5.1 Time-Dependent Versus State-Dependent Versus Event Dependent


   Traffic engineering methodologies can be classified as time-dependent
   or state-dependent. All TE schemes are considered to be dynamic in
   this framework.  Static TE implies that no traffic engineering
   methodology or algorithm is being applied.

   In the time-dependent TE, historical information based on seasonal
   variations in traffic is used to pre-program routing plans and other
   TE control mechanisms.  Additionally, customer subscription or
   traffic projection may be used.  Pre-programmed routing plans
   typically change on a relatively long time scale (e.g., diurnal).



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   Time-dependent algorithms do not attempt to adapt to random
   variations in traffic or changing network conditions. An example of a
   time-dependent algorithm is a global centralized optimizer where the
   input to the system is a traffic matrix and multiclass QoS
   requirements as described [MR99].

   State-dependent TE adapts the routing plans for packets based on the
   current state of the network. The current state of the network
   provides additional information on variations in actual traffic
   (i.e., perturbations from regular variations) that could not be
   predicted using historical information.  Constraint-based routing is
   an example of state-dependent TE operating in a relatively long time
   scale.  An example operating in a relatively short time scale is a
   load-balancing algorithm described in [OMP] and [MATE].

   The state of the network can be based on parameters such as
   utilization, packet delay, packet loss, etc. These parameters can be
   obtained in several ways. For example, each router may flood these
   parameters periodically or by means of some kind of trigger to other
   routers.  Another approach is for a particular router performing
   adaptive TE to send probe packets along a path to gather the state of
   that path.  Still another approach is for a management system to
   gather relevant information from network elements.

   Expeditious and accurate gathering and distribution of state
   information is critical for adaptive TE due to the dynamic nature of
   network conditions.  State-dependent algorithms may be applied to
   increase network efficiency and resilience. Time-dependent algorithms
   are more suitable for predictable traffic variations. On the other
   hand, state-dependent algorithms are more suitable for adapting to
   the prevailing network state.

   Event-dependent TE methods can also be used for TE path selection.
   Event-dependent TE methods are distinct from  time-dependent and
   state-dependent TE methods in the manner in which paths are selected.
   These algorithms are adaptive and distributed in nature and typically
   use learning models to find good paths for TE in a network.  While
   state-dependent TE models typically use available-link-bandwidth
   (ALB) flooding for TE path selection, event-dependent TE methods do
   not require ALB flooding.  Rather, event-dependent TE methods
   typically search out capacity by learning models, as in the success-
   to-the-top (STT) method.  ALB flooding can be resource intensive,
   since it requires link bandwidth to carry LSAs, processor capacity to
   process LSAs, and the overhead can limit area/autonomous system (AS)
   size.  Modeling results suggest that event-dependent TE methods can
   lead to a reduction in ALB flooding overhead without loss of network
   throughput performance [ASH3].

   As an example of event-dependent methods, consider an MPLS network
   that uses a success-to-the-top (STT) event-dependent TE method. In
   this case, if the bandwidth between two label switching routers (say
   LSR-A to LSR-B) needs to be modified, say increased by delta-BW, the
   primary LSP-p is tried first.  If delta-BW is not available on one or
   more links of LSP-p, then the currently successful LSP-s is tried



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   next.  If delta-BW is not available on one or more links of LSP-s,
   then a new LSP is searched by trying additional candidate paths until
   a new successful LSP-n is found or the candidate paths are exhausted.
   LSP-n is then marked as the currently successful path for the next
   time bandwidth needs to be modified.


5.2 Offline Versus Online


   Traffic engineering requires the computation of routing plans.  The
   computation may be performed offline or online.  The computation can
   be done offline for scenarios where routing plans need not be
   executed in real-time.  For example, routing plans computed from
   forecast information may be computed offline.  Typically, offline
   computation is also used to perform extensive searches on multi-
   dimensional solution spaces.

   Online computation is required when the routing plans must adapt to
   changing network conditions as in state-dependent algorithms.  Unlike
   offline computation (which can be computationally demanding), online
   computation is geared toward relative simple and fast calculations to
   select routes, fine-tune the allocations of resources, and perform
   load balancing.


5.3 Centralized Versus Distributed


   Centralized control has a central authority which determines routing
   plans and perhaps other TE control parameters on behalf of each
   router.  The central authority collects the network-state information
   from all routers periodically and returns the routing information to
   the routers.  The routing update cycle is a critical parameter
   directly impacting the performance of the network being controlled.
   Centralized control may need high processing power and high bandwidth
   control channels.

   Distributed control determines route selection by each router
   autonomously based on the routers view of the state of the network.
   The network state information may be obtained by the router using a
   probing method or distributed by other routers on a periodic basis
   using link state advertisements. Network state information may also
   be disseminated under exceptional conditions.


5.4 Local Versus Global


   Traffic engineering algorithms may require local or global network-
   state information.  Note that the scope of network-state information
   does not necessarily refer to the scope of the optimization. In other
   words, it is possible for a TE algorithm to perform global
   optimization based on local state information. Similarly, a TE



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   algorithm may arrive at a locally optimum solution even if it relies
   on global state information.

   Local information pertains to the state of a portion of the domain.
   Examples include the bandwidth and packet loss rate of a particular
   path.  Local state information may be sufficient for certain
   instances of distributed-controlled TEs.

   Global information pertains to the state of the entire domain
   undergoing traffic engineering. Examples include a global traffic
   matrix and loading information on each link throughout the domain of
   interest.  Global state information is typically required with
   centralized control. Distributed TE systems may also need global
   information in some cases.


5.5 Prescriptive Versus Descriptive


   TE systems may also be classified as prescriptive or descriptive.

   Prescriptive traffic engineering evaluates alternatives and
   recommends a course of action. Prescriptive traffic engineering can
   be further categorized as either corrective or perfective. Corrective
   TE prescribes a course of action to address an existing or predicted
   anomaly. Perfective TE prescribes a course of action to evolve and
   improve network performance even when no anomalies are evident.

   Descriptive traffic engineering characterizes, on the other hand, the
   state of the network and assesses the impact of various policies
   without recommending any particular course of action.


5.6 Open-Loop Versus Closed-Loop


   Open-loop traffic engineering control is where control action does
   not use feedback information from the current network state. The
   control action may use its own local information for accounting
   purposes, however.

   Closed-loop traffic engineering control is where control action
   utilizes feedback information from the network state. The feedback
   information may be in the form of historical information or current
   measurement.


5.7 Tactical vs Strategic


   Tactical traffic engineering aims to address specific performance
   problems (such as hot-spots) that occur in the network from a
   tactical perspective, without consideration of overall strategic
   imperatives. Without proper planning and insights, tactical TE tends



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   to be ad hoc in nature.

   Strategic traffic engineering approaches the TE problem from a more
   organized and systematic perspective, taking into consideration the
   immediate and longer term term consequences of specific policies and
   actions.


6.0 Requirements for Internet Traffic Engineering


   This section describes high level requirements and recommendations
   for traffic engineering in the Internet. These requirements are
   presented in very general terms because this is a framework document.
   Additional documents to follow may elaborate on specific aspects of
   these requirements.

   A traffic engineering requirement is a capability needed to solve a
   traffic engineering problem or to achieve a traffic engineering
   objective. Broadly speaking, these requirements can be categorized as
   either non-functional or functional requirements.

   Non-functional requirements for Internet traffic engineering relate
   to the quality attributes or state characteristics of a traffic
   engineering system.  Non-functional traffic engineering requirements
   may contain conflicting assertions and may sometimes be difficult to
   quantify precisely.

   Functional requirements for Internet traffic engineering stipulate
   the functions that a traffic engineering system should perform. These
   functions are needed to realize traffic engineering objectives by
   addressing traffic engineering problems.


6.1 Generic Non-functional Requirements


   The generic non-functional requirements for Internet traffic
   engineering include: usability, automation, scalability, stability,
   visibility, simplicity, efficiency, reliability, survivability,
   correctness, maintainability, extensibility, interoperability, and
   security. In a given context, some of these non-functional
   requirements may be critical while others may be optional. Therefore,
   prioritization may be required during the development phase of a
   traffic engineering system (or components thereof) to tailor it to a
   specific operational context.

   In the following paragraphs, some of the  aspects of the non-
   functional requirements for Internet traffic engineering are
   summarized.

   Usability: Usability is a human factors aspect of traffic engineering
   systems. Usability refers to the ease with which a traffic
   engineering system can be deployed and operated. In general, it is



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   desirable to have a TE system that can be readily deployed in an
   existing network. It is also desirable to have a TE system that is
   easy to operate and maintain.

   Automation: Whenever feasible, a traffic engineering system should
   automate as much of the traffic engineering functions as possible to
   minimize the amount of human effort needed to control and analyze
   operational networks. Automation is particularly imperative in large
   scale public networks because of the high cost of the human aspects
   of network operations and the high risk of network problems caused by
   human errors. Automation may entail the incorporation of automatic
   feedback and intelligence into some components of the traffic
   engineering system.

   Scalability:  Contemporary public networks are growing very fast with
   respect to network size and traffic volume.  Therefore, a TE system
   should be scalable to remain applicable as the network evolves. In
   particular, a TE system should remain functional as the network
   expands with regard to the number of routers and links, and with
   respect to the traffic volume.  A TE system should have a scalable
   architecture, should not adversely impair other functions and
   processes in a network element, and should not consume too much
   network resources when collecting and distributing state information
   or when exerting control.

   Stability:  Stability is a very important consideration in traffic
   engineering systems that respond to changes in the state of the
   network.  State-dependent traffic engineering methodologies typically
   mandate a tradeoff between responsiveness and stability.  It is
   strongly recommended that when tradeoffs are warranted between
   responsiveness and stability, that the tradeoff should be made in
   favor of stability (especially in public IP backbone networks).

   Flexibility: A TE system should be flexible to allow for changes in
   optimization policy. In particular, a TE system should provide
   sufficient configuration options so that a network administrator can
   tailor the TE system to a particular environment.  It may also be
   desirable to have both online and offline TE subsystems which can be
   independently enabled and disabled.  TE systems that are used in
   multi-class networks should also have options to support class based
   performance evaluation and optimization.

   Visibility: As part of the TE system, mechanisms should exist to
   collect statistics from the network and to analyze these statistics
   to determine how well the network is functioning.  Derived statistics
   such as traffic matrices, link utilization, latency, packet loss, and
   other performance measures of interest which are determined from
   network measurements can be used as indicators of prevailing network
   conditions.  Other examples of status information which should be
   observed include existing functional routing information
   (additionally, in the context of MPLS existing LSP routes), etc.

   Simplicity:  Generally, a TE system should be as simple as possible
   consistent with the intended applications. More importantly, the TE



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   system should be relatively easy to use (i.e., clean, convenient, and
   intuitive user interfaces).  Simplicity in user interface does not
   necessarily imply that the TE system will use naive algorithms. Even
   when complex algorithms and internal structures are used, such
   complexities should be hidden as much as possible from the network
   administrator through the user interface.

   Survivability: It is critical for an operational network to recover
   promptly from network failures and to maintain the required QoS for
   existing services.  Survivability generally mandates introducing
   redundancy into the architecture, design, and operation of networks.
   There is a tradeoff between the level of survivability that can be
   attained and the cost required to attain it. The time required to
   restore a network service from a failure depends on several factors,
   including the particular context in which the failure occurred, the
   architecture and design of network, the characteristics of the
   network elements and network protocols, the applications and services
   that were impacted by the failure, etc. The extent and impact of
   service disruptions due to a network failure or outage can vary
   depending on the length of the outage, the part of the network where
   the failure occurred, the type and criticality of the network
   resources that were impaired by the failure, the types of services
   that were impacted by the failure (e.g., voice quality degradation
   following network impairments may be tolerable for an inexpensive
   VoIP service, but may not be tolerable for a toll-quality VoIP
   service). Survivability can be addressed at the device level by
   developing network elements that are more reliable; and at the
   network level by incorporating redundancy into the architecture,
   design, and operation of networks.  It is recommended that a
   philosophy of robustness and survivability should be adopted in the
   architecture, design, and operation of traffic engineering that
   control IP networks (especially public IP networks). Because
   different contexts may demand different levels of survivability, the
   mechanisms developed to support network survivability should be
   flexible so that they can be tailored to different needs.

   Interoperability: Whenever feasible, traffic engineering systems and
   their components should be developed with open standards based
   interfaces to allow interoperation with other systems and components.

   Security: Security is a critical consideration in traffic engineering
   systems that optimize network performance. Such traffic engineering
   systems typical exert control over certain functional aspects of the
   network to achieve the desired performance objectives. Therefore,
   adequate measures must be taken to safeguard the integrity of the
   traffic engineering system. Adequate measures must also be taken to
   protect the network from vulnerabilities that originate from security
   breaches and other impairments within the traffic engineering system.

   The remainder of this section will focus on some of the high level
   functional requirements for traffic engineering.






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6.2 Routing Requirements


   Routing control is a significant aspect of Internet traffic
   engineering.  Routing impacts many of the key performance measures
   associated with networks, such as throughput, delay, and utilization.
   Generally, it is very difficult to provide good service quality in a
   wide area network without effective routing control. A desirable
   routing system is one that takes traffic characteristics and network
   constraints into account during route selection while maintaining
   stability.

   Traditional shortest path first (SPF) interior gateway protocols are
   based on shortest path algorithms and have limited control
   capabilities for traffic engineering [AWD1, AWD2].  These limitations
   include :

   1. The well known issues with pure SPF protocols, which
      do not take network constraints and traffic characteristics
      into account during route selection. For example, since IGPs
      always use the shortest paths (based on administratively
      assigned link metrics) to forward traffic, load sharing cannot
      be accomplished among paths of different costs.  Using shortest
      paths to forward traffic conserves network resources, but may
      cause the following problems: 1) If traffic from a source to a
      destination exceeds the capacity of a link along the shortest
      path, the link (hence the shortest path) becomes congested while
      a longer path between these two nodes may be under-utilized;
      2) the shortest paths from different sources can overlap at some
      links. If the total traffic from the sources exceeds the
      capacity of any of these links, congestion will occur. Problems
      can also occur because traffic demand changes over time but
      network topology and routing configuration cannot be changed as
      rapidly.  This causes the network topology and routing
      configuration to become suboptimal over time, which may result
      in persistent congestion problems.

   2. The Equal-Cost Multi-Path (ECMP) capability of SPF IGPs supports
      sharing of traffic among equal cost paths between two nodes.
      However, ECMP attempts to divide the traffic as equally as
      possible among the equal cost shortest paths. Generally, ECMP
      does not support configurable load sharing ratios among equal
      cost paths.  The result is that one of the paths may carry
      significantly more traffic than other paths because it
      may also carry traffic from other sources. This situation can
      result in congestion along the path that carries more traffic.

   3. Modifying IGP metrics to control traffic routing tends to
      have network-wide effect. Consequently, undesirable and
      unanticipated traffic shifts can be triggered as a result.

   Because of these limitations, new capabilities are needed to enhance
   the routing function in IP networks.  Some of these capabilities have
   been described elsewhere and are summarized below.



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   Constraint-based routing is desirable to evolve the routing
   architecture of IP networks, especially public IP backbones with
   complex topologies [AWD1].  Constraint-based routing computes routes
   to fulfill requirements subject to constraints.  Constraints may
   include bandwidth, hop count, delay, and administrative policy
   instruments such as resource class attributes [AWD1, RFC-2386].  This
   makes it possible to select routes that satisfy a given set of
   requirements subject to network and administrative policy
   constraints. Routes computed through constraint-based routing are not
   necessarily the shortest paths. Constraint-based routing works best
   with path oriented technologies that support explicit routing, such
   as MPLS.

   Constraint-based routing can also be used as a way to redistribute
   traffic onto the infrastructure (even for best effort traffic).  For
   example, if the bandwidth requirements for path selection and
   reservable bandwidth attributes of network links are appropriately
   defined and configured, then congestion problems caused by uneven
   traffic distribution may be avoided or reduced.  In this way, the
   performance and efficiency of the network can be improved.

   A number of enhancements are needed to conventional link state IGPs,
   such as OSPF and IS-IS, to allow them to distribute additional state
   information required for constraint-based routing. The basic
   extensions required are outlined in [Li-IGP]. Specializations of
   these requirements to OSPF were described in [KATZ] and to IS-IS in
   [SMIT].  Essentially, these enhancements require the propagation of
   additional information in link state advertisements. Specifically, in
   addition to normal link-state information, an enhanced IGP is
   required to propagate topology state information needed for
   constraint-based routing. Some of the additional topology state
   information include link attributes such as reservable bandwidth and
   link resource class attribute (an administratively specified property
   of the link). The resource class attribute concept was defined in
   [AWD1].  The additional topology state information is carried in new
   TLVs and sub-TLVs in IS-IS, or in the Opaque LSA in OSPF [SMIT,
   KATZ].

   An enhanced link-state IGP may flood information more frequently than
   a normal IGP. This is because even without changes in topology,
   changes in reservable bandwidth or link affinity can trigger the
   enhanced IGP to initiate flooding.  A tradeoff is typically required
   between the timeliness of the information flooded and the flooding
   frequency to avoid consuming excessive link bandwidth and
   computational resources, and more importantly to avoid instability.

   In a TE system, it is also desirable for the routing subsystem to
   make the load splitting ratio among multiple paths (with equal cost
   or different cost) configurable.  This capability gives network
   administrators more flexibility in the control of traffic
   distribution across the network. It can be very useful for
   avoiding/relieving congestion in certain situations. Examples can be
   found in [XIAO].




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   The routing system should also have the capability to control the
   routes of subsets of traffic without affecting the routes of other
   traffic if sufficient resources exist for this purpose. This
   capability allows a more refined control over the distribution of
   traffic across the network.  For example, the ability to move traffic
   from a source to a destination away from its original path to another
   path (without affecting other traffic paths) allows traffic to be
   moved from resource-poor network segments to resource-rich segments.
   Path oriented technologies such as MPLS inherently support this
   capability as discussed in [AWD2].

   Additionally, the routing subsystem should be able to select
   different paths for different classes of traffic (or for different
   traffic behavior aggregates) if the network supports multiple classes
   of service (different behavior aggregates).


6.3 Traffic Mapping Requirements


   Traffic mapping pertains to the assignment of traffic workload onto
   pre-established paths to meet certain requirements.  Thus, while
   constraint-based routing deals with path selection, traffic mapping
   deals with the assignment of traffic to established paths which may
   have been selected by constraint-based routing or by some other
   means. Traffic mapping can be performed by time-dependent or state-
   dependent mechanisms, as described in Section 5.1.

   An important aspect of the traffic mapping function is the ability to
   establish multiple paths between an originating node and a
   destination node, and the capability to distribute the traffic
   between the two nodes across the paths according to some policies. A
   pre-condition for this scheme is the existence of flexible mechanisms
   to partition traffic and then assign the traffic partitions onto the
   parallel paths. This requirement was noted in [AWD1]. When traffic is
   assigned to multiple parallel paths, it is recommended that special
   care should be taken to ensure proper ordering of packets belonging
   to the same application (or micro-flow) at the destination node of
   the parallel paths.

   As a general rule, mechanisms that perform the traffic mapping
   functions should aim to map the traffic onto the network
   infrastructure to minimize congestion.  If the total traffic load
   cannot be accommodated, or if the routing and mapping functions
   cannot react fast enough to changing traffic conditions, then a
   traffic mapping system may rely on short time scale congestion
   control mechanisms (such as queue management, scheduling, etc) to
   mitigate congestion.  Thus, mechanisms that perform the traffic
   mapping functions should complement existing congestion control
   mechanisms.  In an operational network it is generally desirable to
   map the traffic onto the infrastructure such that intra-class and
   inter-class resource contention are minimized.

   When traffic mapping techniques that depend on dynamic state feedback



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   (e.g. MATE, OMP, and such like) are used, special care must be taken
   to guarantee network stability.


6.4 Measurement Requirements


   The importance of measurement in traffic engineering has been
   discussed throughout this document. Mechanisms should be provided to
   measure and collect statistics from the network to support the
   traffic engineering function.  Additional capabilities may be needed
   to help in the analysis of the statistics.  The actions of these
   mechanisms should not adversely affect the accuracy and integrity of
   the statistics collected. The mechanisms for statistical data
   acquisition should also be able to scale as the network evolves.

   Traffic statistics may be classified according to long-term or
   short-term time scales.  Long-term time scale traffic statistics are
   very useful for traffic engineering. Long-term time scale traffic
   statistics may capture or reflect seasonality in network workload
   (hourly, daily, and weekly variations in traffic profiles) as well as
   traffic trends. Aspects of the monitored traffic statistics may also
   depict class of service characteristics for a network supporting
   multiple classes of service.  Analysis of the long-term traffic
   statistics MAY yield secondary statistics such as busy hour
   characteristics, traffic growth patterns, persistent congestion
   problems, hot-spot, and imbalances in link utilization caused by
   routing anomalies.

   A mechanism for constructing traffic matrices for both long-term and
   short-term traffic statistics should be in place. In multiservice IP
   networks, the traffic matrices may be constructed for different
   service classes.  Each element of a traffic matrix represents a
   statistic of traffic flow between a pair of abstract nodes.  An
   abstract node may represent a router, a collection of routers, or a
   site in a VPN.

   Measured traffic statistics should provide reasonable and reliable
   indicators of the current state of the network on the short-term
   scale. Some short term traffic statistics may reflect link
   utilization and link congestion status. Examples of congestion
   indicators include excessive packet delay, packet loss, and high
   resource utilization.  Examples of mechanisms for distributing this
   kind of information include SNMP, probing techniques, FTP, IGP link
   state advertisements, etc.


6.5 Network Survivability


   Network survivability refers to the capability of a network to
   maintain service continuity in the presence of faults.  This can be
   accomplished by promptly recovering from network impairments and
   maintaining the required QoS for existing services after recovery.



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   Survivability has become an issue of great concern within the
   Internet community due to the increasing demands to carry mission
   critical traffic, real-time traffic, and other high priority traffic
   over the Internet. Failure protection and restoration capabilities
   have become available from multiple layers as network technologies
   have continued to improve. At the bottom of the layered stack,
   optical networks are now capable of providing dynamic ring and mesh
   restoration functionality at the wavelength level as well as
   traditional protection functionality. At the SONET/SDH layer
   survivability capability is provided with Automatic Protection
   Switching (APS) as well as self-healing ring and mesh architectures.
   Similar functionality is provided by layer 2 technologies such as ATM
   (generally with slower mean restoration times). Rerouting is
   traditionally used at the IP layer to restore service following link
   and node outages. Rerouting at the IP layer occurs after a period of
   routing convergence which may require seconds to minutes to complete.
   Some new developments in the MPLS context make it possible to achieve
   recovery at the IP layer prior to convergence.

   To support advanced survivability requirements, path-oriented
   technologies such a MPLS can be used to enhance the survivability of
   IP networks in a potentially cost effective manner. The advantages of
   path oriented technologies such as MPLS for IP restoration becomes
   even more evident when class based protection and restoration
   capabilities are required.

   Recently, a common suite of control plane protocols has been proposed
   for both MPLS and optical transport networks under the acronym
   Multiprotocol Lambda Switching [AWD5]. This new paradigm of
   Multiprotocol Lambda Switching will support even more sophisticated
   mesh restoration capabilities at the optical layer for the emerging
   IP over WDM network architectures.

   Another important aspect regarding multi-layer survivability is that
   technologies at different layers provide protection and restoration
   capabilities at different temporal granularities (in terms of time
   scales) and at different bandwidth granularity (from packet-level to
   wavelength level). Protection and restoration capabilities can also
   be sensitive to different service classes and different network
   utility models.

   The impact of service outages varies significantly for different
   service classes depending upon the effective duration of the outage.
   The duration of an outage can vary from milliseconds (with minor
   service impact) to seconds (with possible call drops for IP telephony
   and session time-outs for connection oriented transactions) to
   minutes and hours (with potentially considerable social and business
   impact).

   Coordinating different protection and restoration capabilities across
   multiple layers in a cohesive manner to ensure network survivability
   is maintained at reasonable cost is a challenging task. Protection
   and restoration coordination across layers may not always be
   feasible, because networks at different layers may belong to



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   different administrative domains.

   The following paragraphs present some of the general requirements for
   protection and restoration coordination.

   - Protection and restoration capabilities from different layers
     should be coordinated whenever feasible and appropriate to
     provide network survivability in a flexible and cost effective
     manner. Minimization of function duplication across layers is
     one way to achieve the coordination. Escalation of alarms and
     other fault indicators from lower to higher layers may also
     be performed in a coordinated manner. A temporal order of
     restoration trigger timing at different layers is another way
     to coordinate multi-layer protection/restoration.


   - Spare capacity at higher layers is often regarded as working
     traffic at lower layers. Placing protection/restoration
     functions in many layers may increase redundancy and robustness,
     but it should not result in significant and avoidable
     inefficiencies in network resource utilization.

   - It is generally desirable to have protection and restoration
     schemes that are bandwidth efficient.

   - Failure notification throughout the network should be timely
     and reliable.

   - Alarms and other fault monitoring and reporting capabilities
     should be provided at appropriate layers.


6.5.1 Survivability in MPLS Based Networks


   MPLS is an important emerging technology that enhances IP networks in
   terms of features, capabilities, and services. Because MPLS is path-
   oriented it can potentially provide faster and more predictable
   protection and restoration capabilities than conventional hop by hop
   routed IP systems. This subsection describes of some of the basic
   aspects and requirements for MPLS networks regarding protection and
   restoration. See [MAK] for a more comprehensive discussion on MPLS
   based recovery.

   Protection types for MPLS networks can be categorized as link
   protection, node protection, path protection, and segment protection.

   - Link Protection: The objective for link protection is to protect
     an LSP from a given link failure. Under link protection, the
     path of the protect or backup LSP (the secondary LSP) is disjoint
     from the path of the working or operational LSP at the particular
     link over which protection is required. When the protected link
     fails, traffic on the working LSP is switched over to the protect
     LSP at the head-end of the failed link. This is a local repair



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     method which can be fast. It might be more appropriate in
     situations where some network elements along a given path are
     less reliable than others.

   - Node Protection: The objective of LSP node protection is to protect
     an LSP from a given node failure. Under node protection, the path
     of   the protect LSP is disjoint from the path of the working LSP
     at the   particular node to be protected. The secondary path is
     also disjoint from the primary path at all links associated with
     the node to be protected. When the node fails, traffic on the
     working LSP is switched over to the protect LSP at the upstream
     LSR directly connected to the failed node.

   - Path Protection: The goal of LSP path protection is to protect an
     LSP from failure at any point along its routed path. Under path
     protection, the path of the protect LSP is completely disjoint from
     the path of the working LSP. The advantage of path protection is
     that the backup LSP protects the working LSP from all possible link
     and node failures along the path, except for failures that might
     occur at the ingress and egress LSRs, or for correlated failures
     that might impact both working and backup paths
     simultaneously. Additionally, since the path selection is
     end-to-end, path protection might be more efficient in terms of
     resource usage than link or node protection.  However, path
     protection may be slower than link and node protection in general.

   - Segment Protection: An MPLS domain may be partitioned into multiple
     protection domains whereby a failure in a protection domain is
     rectified within that domain.  In cases where an LSP traverses
     multiple protection domains, a protection mechanism within a domain
     only needs to protect the segment of the LSP that lies within the
     domain. Segment protection will generally be faster than path
     protection because recovery generally occurs closer to the fault.


6.5.2 Protection Option


   Another issue to consider is the concept of protection options. The
   protection option uses the notation m:n protection where m is the
   number of protect LSPs used to protect n working LSPs. Feasible
   protection options follow.

   - 1:1: one working LSP is protected/restored by one protect LSP.

   - n:1: one working LSP is protected/restored by n protect LSPs,
     possibly with configurable load splitting ratio. When more than
     one protect LSP is used, it may be desirable to share the traffic
     across the protect LSPs when the working LSP fails to satisfy the
     bandwidth requirement of the traffic trunk associated with the
     working LSP. This may be especially useful when it is not feasible
     to find one path that can satisfy the the bandwidth requirement of
     the primary LSP.




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   - 1:n: one protection LSP is used to protect/restore n working LSPs.

   - 1+1: traffic is sent concurrently on both the working LSP and the
     protect LSP. In this case, the egress LSR selects one of the two
     LSPs based on a local traffic integrity decision process, which
     compares the traffic received from both the working and the protect
     LSP and identifies discrepancies.  It is unlikely that this option
     would be used extensively in IP networks due to its resource
     utilization inefficiency. However, if bandwidth becomes plentiful
     and cheap, then this option might become quite viable and
     attractive in IP networks.


6.5.3 Resilience Attributes


   Resilience attributes can be associated with explicit label switched
   in MPLS domains to indicate the manner in which traffic flowing
   through the LSP is restored when the LSP fails. These attributes can
   be categorized into basic attributes and extended attributes. The
   concept of resilience attributes within the MPLS context was first
   described in [AWD1].

   Basic resilience attributes can indicate whether the traffic through
   an LSP can be rerouted using the IGP or mapped onto protect LSP(s)
   when a segment of the working path fails. A basic resilience
   attribute may also indicate that no rerouting is to occur at all.

   Extended resilience attributes can be used to specify more
   sophisticated recovery options. Some feasible options are described
   below:

   1. Protection LSP establishment attribute: Indicates whether the
      protect LSP is  pre-established or established-on-demand
      after receiving a failure notification. A pre-established
      protect LSP can restore service faster, while an
      established-on-demand LSP is more likely to find a more
      efficient path with respect to resource usage. In the case
      of pre-established LSPs, if a fault impacts the working and
      protect LSPs simultaneously, it might not be feasible to
      restore the affected traffic if an alternative mechanism does
      not exist.

   2. Constraint attribute under failure condition: Indicates whether
      the protect LSP requires certain constraint(s) to be satisfied
      in order for it to be established.  These constraints can be the
      same or less than the ones used to establish the primary LSP
      under normal conditions, e.g., bandwidth requirement, or no
      bandwidth requirement may be indicated under failure conditions.

   3. Protection LSP resource reservation attribute: Indicates whether
      resource allocation for a pre-established protection LSP is
      reserved a priori or reserved-on-demand after failure
      notification is received.



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   We now discuss the relative merits of the resilience attributes. A
   pre-established protection LSP with pre-reserved resources can
   guarantee that the QoS of existing services is maintained upon
   failure of the primary LSP, while a pre-established and reserve-on-
   demand or an established-on-demand LSP may not be able to guarantee
   the QoS.  The pre-established and pre-reserved approach is also the
   fastest among the three. It can switch packets onto the protection
   LSP once the ingress LSR receives the failure notification message
   without experiencing any delay for routing, resource allocation, and
   LSP establishment. However, a pre-established protection LSP may not
   be able to adapt to changes in the network since it cannot be re-
   established if a better path becomes available due to changes in the
   network. Additionally, the bandwidth reserved on the protection LSP
   is subtracted from the available bandwidth pool on all associated
   links, so it is not available for instantiating new LSPs in the
   future. On the other hand, it differs from SONET protection in that
   the reserved bandwidth does not remain under utilized.  Instead, when
   deployed in an IP context, it can be used by any traffic present on
   those links.  When pre-established protection LSP and established-
   on-demand LSp are compared, the it can be seen that the former will
   tend to restore traffic faster because there is no need to wait for
   the path to be set up prior to switching over traffic. However, if
   the requested bandwidth is not available on the pre-established path,
   it may be possible to use an established-on-demand LSP as a secondary
   option.

   Failure Notification:

   Failure notification should be reliable and fast, i.e., at least as
   fast as IGP notification, but preferably faster.


6.6 Content Distribution (Webserver) Requirements


   The Internet is dominated by client-server interactions, especially
   Web traffic (in the future, more sophisticated media servers may
   become dominant). The location of major information servers has a
   significant impact on the traffic patterns within the Internet as
   well as on the perception of service quality by end users.

   A number of dynamic load balancing techniques have been devised to
   improve the performance of replicated information servers. These
   techniques can cause spatial traffic characteristics to become more
   dynamic in the Internet because information servers can be
   dynamically picked based upon the location of the clients, the
   location of the servers, the relative utilization of the servers, the
   relative performance of different networks, and the relative
   performance of different parts of a network.  This process of
   assignment of distributed servers to clients is called Traffic
   Directing (TD).  It is similar to traffic engineering but operates at
   the application layer.

   TD scheduling schemes that allocate servers to clients in replicated,



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   geographically dispersed information distribution systems may require
   empirical network performance statistics to make more effective
   decisions.  In the future, network measurement systems may be
   required to provide this type of information.  The exact parameters
   needed are not yet defined. When congestion exists in the network,
   the TD and TE systems should act in a coordinated manner. This topic
   is for further study.

   Network planning should take into consideration the fact that TD can
   introduce more traffic dynamics into a network.  It can be helpful
   for a certain amount of additional link capacity to be reserved so
   that the links can accommodate this additional traffic fluctuation.


6.7 Traffic Engineering in Diffserv Environments


   This section provides an overview of the traffic engineering features
   and requirements that are specifically pertinent to Differentiated
   Services (Diffserv) capable IP networks.

   Increasing requirements to support multiple classes of traffic, such
   as best effort and mission critical data, in the Internet calls for
   IP networks to differentiate traffic according to some criteria, and
   to accord preferential treatment to certain types of traffic.  Large
   numbers of flows can be aggregated into a few behavior aggregates
   based on some criteria in terms of common performance requirements in
   terms of packet loss ratio, delay, and jitter; or in terms of common
   fields within the IP packet headers.

   As Diffserv evolves and becomes deployed in operational networks,
   traffic engineering will be critical to ensuring that SLAs defined
   within a given Diffserv service model are met. Classes of service
   (CoS) can be supported in a Diffserv environment by concatenating
   per-hop behaviors (PHBs) along the routing path, using service
   provisioning mechanisms, and by appropriately configuring edge
   functionality such as traffic classification, marking, policing, and
   shaping. PHB is the forwarding behavior that a packet receives at a
   DS node (a Diffserv-compliant node). This is accomplished by means of
   buffer management and packet scheduling mechanisms. In this context,
   packets belonging to a class are those that are members of a
   corresponding ordering aggregate.

   In order to provide enhanced quality of service in a Diffserv domain,
   it is simply not enough to implement proper buffer management and
   scheduling mechanisms. Instead, in addition to buffer management and
   scheduling mechanisms, it may be desirable to control the performance
   of some service classes by enforcing certain relationships between
   the traffic workload contributed by each service class and the amount
   of network resources allocated or provisioned for that service class.
   Such relationships between demand and resource allocation can be
   enforced using a combination of, for example: (1) traffic engineering
   mechanisms that enforce the desired relationship between the amount
   of traffic contributed by a given service class and the resources



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   allocated to that class and (2) mechanisms that dynamically adjust
   the resources allocated to a give service class to relate to the
   amount of traffic contributed by that service class.

   It may also be desirable to limit the performance impact of high
   priority traffic on relatively low priority traffic. This can be
   achieved by, for example, controlling the percentage of high priority
   traffic that is routed through a given link. Another way to
   accomplish this is to increase link capacities appropriately so that
   lower priority traffic can still enjoy adequate service quality. When
   the ratio of traffic workload contributed by different service
   classes vary significantly from router to router, it may not suffice
   to rely exclusively on conventional IGP routing protocols or on
   traffic engineering mechanisms that are insensitive to different
   service classes.  Instead, it may be desirable to perform traffic
   engineering, especially routing control and mapping functions, on a
   per service class basis. One way to accomplish this in a domain that
   supports both MPLS and Diffserv is to define class specific LSPs and
   to map traffic from each class onto one or more LSPs that correspond
   to that service class. An LSP corresponding to a given service class
   can then be routed and protected/restored in a class dependent
   manner, according specific policies.

   Performing traffic engineering on a per class basis in multi-class IP
   networks might be beneficial in terms of both performance and
   scalability. It allows traffic trunk in a given class to utilize
   available resources on both shortest path(s) and non-shortest paths
   that meet constraints and requirements that are specific to the given
   class.  MPLS is capable of providing different levels of
   protection/restoration mechanisms, from the fastest link/node
   protection to path protection which can be pre-established with or
   without pre-reserved resources or established-on-demand. The faster a
   mechanism, the more it costs to network resources. By performing
   per-class protection/restoration, each class can select some
   protection/restoration mechanisms that satisfy its survivability
   requirements in a cost effective manner.

   The following paragraphs describe very high level requirements that
   are specific to the control of traffic trunks in Diffserv/MPLS
   environments. These are additional to the general requirements for
   traffic engineering over MPLS described in [AWD1].

   - An LSR should provide configurable maximum reservable bandwidth
   and/or buffer for each supported service class (Ordering Aggregate).

   - An LSR should provide configurable minimum available bandwidth
   and/or buffer for each class on each of its links.

   - In order to perform constraint-based routing on a per-class basis
   for LSPs, the conventional IGPs (e.g., IS-IS and OSPF) should provide
   extensions to propagate per-class resource information.

   - In contexts where delay bounds are a factor, then path selection
   algorithms for traffic trunks with bounded delay requirements should



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   take into account delay constraint.  Delay consists mainly
   serialization delay, propagation delay, (which is fixed for a given
   path), and queuing delay (which varies). In practice, it is quite to
   estimate delays analytically. Delay models are contemporary research
   topics. In practice, the queuing delay can be approximated using
   estimates of fixed per-hop queuing delay bound at each hop for each
   PHB.

   - When an LSR dynamically adjusts resource allocation based on per-
   class LSP resource requests, adjustment of weight used by scheduling
   algorithms should not adversely impact the delay and jitter
   characteristics of certain service classes.

   - An LSR should provide configurable maximum allocation multiplier on
   a per-class basis.

   - Measurement-based admission control may be used to improve resource
   usage, especially for those classes without stringent loss or delay
   and jitter requirements. For example, an LSR may dynamically adjust
   maximum allocation multiplier (i.e., over-subscribing and under-
   subscribing ratios) for certain classes based on their resource
   measured utilization.

   Instead of having per-class parameters being configured and
   propagated on each LSR interface, per-class parameters can be
   aggregated into per-class-type parameters. The main motivation for
   grouping a set of classes into a class-type is to improve the
   scalability of IGP link state advertisements by propagating
   information on a per-class-type basis instead of on a per-class
   basis, and also to allow better bandwidth sharing between  classes in
   the same class-type. A class-type is a set of classes that satisfy
   the following two conditions:

   1) Classes in the same class-type have common aggregate maximum or
   minimum bandwidth requirements to satisfy required performance
   levels.

   2) There is no maximum or minimum bandwidth requirement to be
   enforced at the level of individual class in the class-type. It is
   still, nevertheless, to implement some "priority" policies for
   classes in the same class-type to permit preferential access to the
   class-type bandwidth.

   An example of the class-type can be a low-loss class-type that
   includes both AF1-based and AF2-based Ordering Aggregates. With such
   a class-type, one may implement some priority policy which assigns
   higher preemption priority to AF1-based traffic trunks over AF2-based
   ones, vice versa, or the same priority.


6.8 Network Controllability


   Off-line (and on-line) traffic engineering considerations would be of



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   limited utility if the network could not be controlled effectively to
   implement the results of TE decisions and to achieve desired network
   performance objectives. Capacity augmentation is a coarse grained
   solution to traffic engineering issues. However, it is simple and may
   be advantageous if bandwidth is abundant and cheap or if the current
   or expected network workload demands it. However, bandwidth is not
   always abundant and cheap, and the workload may not always demand
   additional capacity. Adjustments of administrative weights and other
   parameters associated with routing protocols provide finer grained
   control, but is difficult to use and imprecise because of the routing
   interactions that occur across the network. In certain network
   contexts, more flexible, finer grained approaches which provide more
   precise control over the mapping of traffic to routes and over the
   the selection and placement of routes may be appropriate and useful.

   Control mechanisms can be manual (e.g. administrative configuration),
   partially-automated (e.g. scripts) or fully-automated (e.g. policy
   based management systems). Automated mechanisms are particularly
   required in large scale networks.  Multi-vendor interoperability can
   be facilitated by developing and deploying standardized management
   systems (e.g. standard MIBs) and policies (PIBs) to support the
   control functions required to address traffic engineering objectives
   such as load distribution and protection/restoration.

   Network control functions should be secure, reliable, and stable as
   these are often needed to operate correctly in times of network
   impairments (e.g. during network congestion or security attacks).


7.0 Inter-Domain Considerations


   Inter-domain traffic engineering is concerned with the performance
   optimization for traffic that originates in one administrative domain
   and terminates in a different one.

   Traffic exchange between autonomous systems in the Internet occurs
   through exterior gateway protocols. Currently, BGP-4 [bgp4] is the
   standard exterior gateway protocol for the Internet.  BGP-4 provides
   a number of capabilities that can be used to define import and export
   policies for network reachability information. BGP attributes are
   used by the BGP decision process to select exit points for traffic to
   other peer networks.

   Inter-domain traffic engineering is inherently more difficult than
   intra-domain TE under the current Internet architecture. The reasons
   for this are both technical and administrative. Technically, the
   current version of BGP does not propagate topology and link state
   information across domain boundaries. There are stability and
   scalability issues involved in propagating such details, which
   require careful consideration. Administratively, there are
   differences in operating costs and network capacities between
   domains. Generally, what may be considered a good solution in one
   domain may not necessarily be a good solution in another domain.



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   Moreover, it would generally be considered inadvisable for one domain
   to permit another domain to influence the routing and management of
   traffic in its network.

   If Diffserv becomes widely deployed, inter-domain TE will become more
   important, and more challenging to address.

   MPLS TE-tunnels (explicit LSPs) can potentially add a degree of
   flexibility in the selection of exit points for inter-domain routing.
   The concept of relative and absolute metrics defined in [SHEN] can be
   applied to this purpose. The idea is that if BGP attributes are
   defined such that the BGP decision process depends on IGP metrics to
   select exit points for Inter-domain traffic, then some inter-domain
   traffic destined to a given peer network can be made to prefer a
   specific exit point by establishing a TE-tunnel between the router
   making the selection to the peering point via a TE-tunnel and
   assigning the TE-tunnel a metric which is smaller than the IGP cost
   to all other peering points. If a peer accepts and processes MEDs,
   then a similar MPLS TE-tunnel based scheme can be applied to cause
   certain entrance points to be preferred by setting MED to be an IGP
   cost, which has been modified by the tunnel metric.

   Similar to intra-domain TE, Inter-domain TE is best accomplished when
   a traffic matrix can be derived to depict the volume of traffic from
   one autonomous system to another.

   Generally, redistribution of inter-domain traffic requires
   coordination between peering partners. An export policy in one domain
   that results in load redistribution across peer points with another
   domain can significantly affect the local traffic matrix inside the
   domain of the peering partner. This, in turn, will affect the intra-
   domain TE due to changes in the spatial distribution traffic.
   Therefore, it is critical for peering partners to coordinate with
   each other before attempting any policy changes that may result in
   significant shifts in inter-domain traffic. In certain contexts, this
   coordination can be quite challenging due to technical and non-
   technical reasons.

   It is a matter of speculation as to whether MPLS, or similar
   technologies, can be extended to allow selection of constrained-paths
   across domain boundaries.


8.0 Overview of  Contemporary TE Practices in Operational IP Networks


   This section provides an overview of some contemporary traffic
   engineering practices in IP networks. The focus is primarily on the
   aspects that pertain to the control of the routing function in
   operational contexts. The intent here is to provide an overview of
   the commonly used practices. The discussion is not intended to be
   exhaustive.

   Currently, service providers apply many of the traffic engineering



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   mechanisms discussed in this document to optimize the performance of
   their IP networks. These techniques include capacity planning for
   long time scales, routing control using IGP metrics and MPLS for
   medium time scales, the overlay model also for medium time scales,
   and traffic management mechanisms for short time scale.

   When a service provider plans to build an IP network, or expand the
   capacity of an existing network, effective capacity planning should
   be an important component of the process. Such plans may take the
   following aspects into account: location of new nodes if any,
   existing and predicted traffic patterns, costs, link capacity,
   topology, routing design, and survivability.

   Performance optimization of operational networks is usually an
   ongoing process in which traffic statistics, performance parameters,
   and fault indicators are continually collected from the network.
   These empirical data are then analyzed and used to trigger various
   traffic engineering mechanisms. For example, IGP parameters, e.g.,
   OSPF or IS-IS metrics, can be adjusted based on manual computations
   or based on the output of some traffic engineering support tools.
   Such tools may use the following as input the: traffic matrix,
   network topology, and network performance objective(s). Tools that
   perform what-if analysis can also be used to assist the TE process by
   allowing various scenarios to be reviewed before a new set of
   configurations are implemented in the operational network.

   The overlay model (IP over ATM or IP over Frame relay) is another
   approach which is commonly used in practice [AWD2]. The IP over ATM
   technique is no longer viewed favorably due to recent advances in
   MPLS and router hardware technology.

   Deployment of MPLS for traffic engineering applications has commenced
   in some service provider networks.  One operational scenario is to
   deploy MPLS in conjunction with an IGP (IS-IS-TE or OSPF-TE) that
   supports the traffic engineering extensions, in conjunction with
   constraint-based routing for explicit route computations, and a
   signaling protocol (e.g. RSVP-TE or CRLDP) for LSP instantiation.

   In contemporary MPLS traffic engineering contexts, network
   administrators specify and configure link attributes and resource
   constraints such as maximum reservable bandwidth and resource class
   attributes for links (interfaces) within the MPLS domain. A link
   state protocol that supports TE extensions (IS-IS-TE or OSPF-TE) is
   used to propagate information about network topology and link
   attribute to all routers in the routing area.  Network administrators
   also specify all the LSPs that are to originate each router. For each
   LSP, the network administrator specifies the destination node and the
   attributes of the LSP which indicate the requirements that to be
   satisfied during the path selection process. Each router then uses a
   local constraint-based routing process to compute explicit paths for
   all LSPs originating from it. Subsequently, a signaling protocol is
   used to instantiate the LSPs. By assigning proper bandwidth values to
   links and LSPs, congestion caused by uneven traffic distribution can
   generally be avoided or mitigated.



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   The bandwidth attributes of LSPs used for traffic engineering can be
   updated periodically. The basic concept is that the bandwidth
   assigned to an LSP should relate in some manner to the bandwidth
   requirements of traffic that actually flows through the LSP. The
   traffic attribute of an LSP can be modified to accommodate traffic
   growth and persistent traffic shifts. If network congestion occurs
   due to some unexpected events, existing LSPs can be rerouted to
   alleviate the situation or network administrator can configure new
   LSPs to divert some traffic to alternative paths. The reservable
   bandwidth of the congested links can also be reduced to force some
   LSPs to be rerouted to other paths.

   In an MPLS domain, a traffic matrix can also be estimated by
   monitoring the traffic on LSPs. Such traffic statistics can be used
   for a variety of purposes including network planning and network
   optimization. Current practice suggests that deploying an MPLS
   network consisting of hundreds of routers and thousands of LSPs is
   feasible. In summary, recent deployment experience suggests that MPLS
   approach is very effective for traffic engineering in IP networks
   [XIAO].


9.0 Conclusion


   This document described a framework for traffic engineering in the
   Internet.  It presented an overview of some of the basic issues
   surrounding traffic engineering in IP networks. The context of TE was
   described, a TE process models and a taxonomy of TE styles were
   presented.  A brief historical review of pertinent developments
   related to traffic engineering was provided. A survey of contemporary
   TE techniques in operational networks was presented. Additionally,
   the document specified a set of generic requirements,
   recommendations, and options for Internet traffic engineering.


10.0 Security Considerations


   This document does not introduce new security issues.


11.0 Acknowledgments


   The authors would like to thank Jim Boyle for inputs on the
   requirements section, Francois Le Faucheur for inputs on Diffserv
   aspects, Blaine Christian for inputs on measurement, Gerald Ash for
   inputs on routing in telephone networks and for text on event-
   dependent TE methods , and Steven Wright for inputs on network
   controllability.  Special thanks to Randy Bush for proposing the TE
   taxonomy based on "tactical vs strategic" methods. The subsection
   describing an "Overview of ITU Activities Related to Traffic
   Engineering" was adapted from a contribution by Waisum Lai.  Useful



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   feedback and pointers to relevant materials were provided by J. Noel
   Chiappa. Finally, the authors would like to thank Ed Kern, the TEWG
   co-chair, for his comments and support.


12.0 References


   [ASH1] J. Ash, M. Girish, E. Gray, B. Jamoussi, G. Wright,
   "Applicability Statement for CR-LDP," Work in Progress, 1999.

   [ASH2] J. Ash, Dynamic Routing in Telecommunications Networks, McGraw
   Hill, 1998

   [ASH3] TE & QoS Methods for IP-, ATM-, & TDM-Based Networks, <draft-
   ash-te-qos-routing-01.txt>, Work in Progress, 2000.

   [AWD1] D. Awduche, J. Malcolm, J. Agogbua, M. O'Dell, J. McManus,
   "Requirements for Traffic Engineering over MPLS," RFC 2702, September
   1999.

   [AWD2] D. Awduche, "MPLS and Traffic Engineering in IP Networks,"
   IEEE Communications Magazine, December 1999.

   [AWD3] D. Awduche, L. Berger, D. Gan, T. Li, G. Swallow, and V.
   Srinivasan "Extensions to RSVP for LSP Tunnels," Work in Progress,
   1999.

   [AWD4] D. Awduche, A. Hannan, X. Xiao, " Applicability Statement for
   Extensions to RSVP for LSP-Tunnels" Work in Progress, 1999.

   [AWD5] D. Awduche et al, "An Approach to Optimal Peering Between
   Autonomous Systems in the Internet," International Conference on
   Computer Communications and Networks (ICCCN'98), October 1998.

   [AWD6] D. Awduche, Y. Rekhter, J. Drake, R. Coltun, "Multiprotocol
   Lambda Switching: Combining MPLS Traffic Engineering Control with
   Optical Crossconnects," Work in Progress, 1999.

   [CAL] R. Callon, P. Doolan, N. Feldman, A. Fredette, G. Swallow, A.
   Viswanathan, A Framework for Multiprotocol Label Switching," Work in
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   [FGLR] A. Feldmann, A. Greenberg, C. Lund, N. Reingold, and J.
   Rexford, "NetScope: Traffic Engineering for IP Networks," to appear
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   [FlJa93] S. Floyd and V. Jacobson, "Random Early Detection Gateways
   for Congestion Avoidance", IEEE/ACM Transactions on Networking, Vol.
   1 Nov. 4., August 1993, p. 387-413.

   [FLoyd2000] S. Floyd, "Congestion Control Principles," Work in
   Progress, 2000.




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   [Floy94] S. Floyd, "TCP and Explicit Congestion Notification", ACM
   Computer Communication Review, V. 24, No. 5, October 1994, p. 10-23.

   [HuSS87] B.R. Hurley, C.J.R. Seidl and W.F. Sewel, "A Survey of
   Dynamic Routing Methods for Circuit-Switched Traffic", IEEE
   Communication Magazine, Sep 1987.

   [itu-e600] ITU-T Recommendation E.600, "Terms and Definitions of
   Traffic Engineering", March 1993.

   [itu-e701] ITU-T Recommendation E.701 "Reference Connections for
   Traffic Engineering", October 1993.

   [JAM] B. Jamoussi, "Constraint-Based LSP Setup using LDP," Work in
   Progress, 1999.

   [Li-IGP] T. Li, G. Swallow, and D. Awduche, "IGP Requirements for
   Traffic Engineering with MPLS," Work in Progress, 1999

   [Berger] L. Berger, D. Gan, G. Swallow, P. Pan, F. Tommasi, and S.
   Molendini "RSVP Refresh Overhead Reduction Extensions," Work in
   Progress, 2000

   [LNO96] T. Lakshman, A. Neidhardt, and T. Ott, "The Drop from Front
   Strategy in TCP over ATM and its Interworking with other Control
   Features", Proc. INFOCOM'96, p. 1242-1250.

   [MATE] I. Widjaja and A. Elwalid, "MATE: MPLS Adaptive Traffic
   Engineering," Work in Progress, 1999.

   [ELW95] A. Elwalid, D. Mitra and R.H. Wentworth, "A New Approach for
   Allocating Buffers and Bandwidth to Heterogeneous, Regulated Traffic
   in an ATM Node," IEEE IEEE Journal on Selected Areas in
   Communications, 13:6, August 1995, pp. 1115-1127.

   [Cruz] R. L. Cruz, "A Calculus for Network Delay, Part II: Network
   Analysis,'' IEEE Transactions on Information Theory, vol. 37, pp.
   132--141, 1991.

   [McQ80] J.M. McQuillan, I. Richer, and E.C. Rosen, "The New Routing
   Algorithm for the ARPANET", IEEE. Trans. on Communications, vol. 28,
   no. 5, pp. 711-719, May 1980.

   [RFC-1992] I. Castineyra, N. Chiappa, and M. Steenstrup, "The Nimrod
   Routing Architecture," RFC-1992, August 1996.

   [MR99] D. Mitra and K.G. Ramakrishnan, "A Case Study of Multiservice,
   Multipriority Traffic Engineering Design for Data Networks, Proc.
   Globecom'99, Dec 1999.

   [OMP] C. Villamizar, "MPLS Optimized OMP", Work in Progress, 1999.

   [RFC-1349] P. Almquist, "Type of Service in the Internet Protocol
   Suite", RFC 1349, Jul 1992.



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   [RFC-1458] R. Braudes, S. Zabele, "Requirements for Multicast
   Protocols," RFC 1458, May 1993.

   [RFC-1771] Y. Rekhter and T. Li, "A Border Gateway Protocol 4 (BGP-
   4), RFC 1771, March 195.

   [RFC-1812] F. Baker (Editor), "Requirements for IP Version 4
   Routers," RFC 1812, June 1995.

   [RFC-1997] R. Chandra, P. Traina, and T. Li, "BGP Community
   Attributes" RFC 1997, August 1996.

   [RFC-1998] E. Chen and T. Bates, "An Application of the BGP Community
   Attribute in Multi-home Routing," RFC 1998, August 1996.

   [RFC-2178] J. Moy, "OSPF Version 2", RFC 2178, July 1997.

   [RFC-2205] R. Braden, et. al., "Resource Reservation Protocol (RSVP)
   - Version 1 Functional Specification", RFC 2205, September 1997.

   [RFC-2211] J. Wroclawski, "Specification of the Controlled-Load
   Network Element Service", RFC 2211, Sep 1997.

   [RFC-2212] S. Shenker, C. Partridge, R. Guerin, "Specification of
   Guaranteed Quality of Service," RFC 2212, September 1997

   [RFC-2215] S. Shenker, and J. Wroclawski, "General Characterization
   Parameters for Integrated Service Network Elements", RFC 2215,
   September 1997.

   [RFC-2216] S. Shenker,  and J. Wroclawski, "Network Element Service
   Specification Template", RFC 2216, September 1997.

   [RFC-2330] V. Paxson et al., "Framework for IP Performance Metrics",
   RFC 2330, May 1998.

   [RFC-2386] E. Crawley, R. Nair, B. Rajagopalan, and H. Sandick, "A
   Framework for QoS-based Routing in the Internet", RFC 2386, Aug.
   1998.

   Q. Ma, "Quality of Service Routing in Integrated Services Networks,"
   PhD Dissertation, CMU-CS-98-138, CMU, 1998.

   [RFC-2475] S. Blake et al., "An Architecture for Differentiated
   Services", RFC 2475, Dec 1998.

   [RFC-2597] J. Heinanen, F. Baker, W. Weiss, and J. Wroclawski,
   "Assured Forwarding PHB Group", RFC 2597, June 1999.

   [RFC-2678] J. Mahdavi and V. Paxson, "IPPM Metrics for Measuring
   Connectivity", RFC 2678, Sep 1999.

   [RFC-2679] G. Almes, S. Kalidindi, and M. Zekauskas, "A One-way Delay
   Metric for IPPM", RFC 2679, Sep 1999.



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   [RFC-2680] G. Almes, S. Kalidindi, and M. Zekauskas, "A One-way
   Packet Loss Metric for IPPM", RFC 2680, Sep 1999.

   [RFC-2722] N. Brownlee, C. Mills, and G. Ruth, "Traffic Flow
   Measurement: Architecture", RFC 2722, Oct 1999.

   [RFC-2753] R. Yavatkar, D. Pendarakis, R. Guerin, "A Framework for
   Policy-based Admission Control, RFC 2753, January 2000.

   [RoVC] E. Rosen, A. Viswanathan, R. Callon, "Multiprotocol Label
   Switching Architecture," Work in Progress, 1999.

   [SLDC98] B. Suter, T. Lakshman, D. Stiliadis, and A. Choudhury,
   "Design Considerations for Supporting TCP with Per-flow Queueing",
   Proc. INFOCOM'99, 1998, p. 299-306.

   [MAK] S. Makam, et. al., "Framework for MPLS Based Recovery", Work in
   Progress, 2000.

   [XIAO] X. Xiao, A. Hannan, B. Bailey, L. Ni, "Traffic Engineering
   with MPLS in the Internet", IEEE Network magazine, March 2000.

   [YaRe95] C. Yang and A. Reddy, "A Taxonomy for Congestion Control
   Algorithms in Packet Switching Networks", IEEE Network Magazine, 1995
   p. 34-45.

   [SMIT] H. Smit and T. Li, "IS-IS extensions for Traffic
   Engineering,"Internet Draft, Work in Progress, 1999

   [KATZ] D. Katz, D. Yeung, "Traffic Engineering Extensions to
   OSPF,"Internet Draft, Work in Progress, 1999

   [SHEN] N. Shen and H. Smit, "Calculating IGP routes over Traffic
   Engineering tunnels" Internet Draft, Work in Progress, 1999.























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13.0 Authors' Addresses:


      Daniel O. Awduche
      UUNET (MCI Worldcom)
      22001 Loudoun County Parkway
      Ashburn, VA 20147
      Phone: 703-886-5277
      Email: awduche@uu.net

      Angela Chiu
      AT&T Labs
      Rm 4-204,
      100 Schulz Dr.
      Red Bank, NJ 07701
      Phone: (732) 345-3441
      Email: alchiu@att.com

      Anwar Elwalid
      Lucent Technologies
      Murray Hill, NJ 07974, USA
      Phone: 908 582-7589
      Email: anwar@lucent.com

      Indra Widjaja
      Fujitsu Network Communications
      Two Blue Hill Plaza
      Pearl River, NY 10965, USA
      Phone: 914-731-2244
      Email: indra.widjaja@fnc.fujitsu.com

      Xipeng Xiao
      Global Crossing
      141 Caspian Court,
      Sunnyvale, CA 94089
      Email: xipeng@globalcenter.net
      Voice: +1 408-543-4801




















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