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Traffic Engineering Working Group                           Wai Sum Lai
Internet Draft                                                AT&T Labs
Document: <draft-ietf-tewg-measure-03.txt>
Category: Informational                                Blaine Christian

                                                       Richard W. Tibbs
                                                    Oak City Networks &

                                                  Steven Van den Berghe
                                                  Ghent University/IMEC

                                                         September 2002

         A Framework for Internet Traffic Engineering Measurement

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
   other groups may also distribute working documents as Internet-
   Drafts. Internet-Drafts are draft documents valid for a maximum of
   six months and may be updated, replaced, or obsoleted by other
   documents at any time. It is inappropriate to use Internet- Drafts
   as reference material or to cite them other than as "work in

   The list of current Internet-Drafts can be accessed at

   The list of Internet-Draft Shadow Directories can be accessed at

1. Abstract

   In this document, a measurement framework for supporting the traffic
   engineering of IP-based networks is presented.  Uses of traffic
   measurement in service provider environments are described, and
   issues related to time scale and read-out period are discussed.
   Different measurement types are classified, with each being
   specified as a meaningful combination of a measurement entity and a
   measurement basis.

   For interoperable compatibility, uniform definitions across vendors
   and operators must be ensured, e.g., in the distinction between
   offered load and achieved throughput.  To aid network dimensioning,
   mechanisms to collect node-pair-based traffic data should be
   developed to facilitate the derivation of per-service-class traffic

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   matrix statistics.  For service assurance, there is a need for the
   use of higher-order statistics.  To preserve representative traffic
   detail at manageable sample volumes, there is a need for packet-
   sampled measurements.  To manage large volume of measured data, use
   of bulk transfer and filtering/aggregation mechanisms may be

Table of Contents

   Status of this Memo................................................1
   1. Abstract........................................................1
   2. Conventions used in this document...............................2
   3. Introduction....................................................3
   4. Terminology.....................................................4
   4.1 Route, path....................................................4
   4.2 Throughput, traffic volume.....................................4
   5. Uses of Traffic Measurement.....................................5
   5.1 Traffic characterization.......................................5
   5.2 Network monitoring.............................................6
   5.3 Traffic control................................................6
   6. Time Scales for Network Operations..............................6
   7. Read-Out Periods................................................7
   8. Measurement Bases...............................................8
   8.1 Flow-based.....................................................9
   8.2 Interface-based, link-based, node-based........................9
   8.3 Node-pair-based...............................................10
   8.4 Path-based....................................................10
   9. Measurement Entities...........................................11
   9.1 Entities related to traffic and performance...................11
   9.2 Entities related to establishment of connection or path.......13
   10. Measurement Types.............................................13
   10.1 Measurement types related to traffic or performance..........14
   10.2 Measurement types related to resource usage..................14
   11. Traffic Matrix Statistics.....................................15
   12. Performance Monitoring........................................16
   13. Packet Sampling...............................................17
   14. Statistical Estimation and Information Modeling...............18
   14.1 Engineering methods for statistical estimation of measures...18
   14.2 TE Measure Information Modeling..............................18
   15. Conclusions and Recommendations...............................20
   16. Security Considerations.......................................21
   17. References....................................................21
   18. Intellectual Property Statement...............................23
   19. Acknowledgments...............................................24
   20. Author's Addresses............................................24
   Full Copyright Statement..........................................24

2. Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   this document are to be interpreted as described in RFC-2119.

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3. Introduction

   This document describes a framework for Internet traffic engineering
   measurement, with the objective of providing principles for the
   development of a set of measurement systems to support the traffic
   engineering of IP-based networks [1].  A major goal is to provide
   guidance for establishing protocol-independent and platform-neutral
   traffic measurement standards to achieve multi-vendor inter-
   operability.  It is critical to minimize the possibilities of
   inconsistencies arising from, e.g., differing statistical
   definitions, overlapping data collection, processing at different
   protocol levels, and similar inconsistencies by different vendors or
   network operators.

   The need for a common framework, including detailed definitions for
   measurements, is motivated by the needs for consistency, precision,
   and effectiveness of the overall traffic engineering function.
   Traffic engineering includes measurements, forecasting, planning,
   dimensioning, control, and performance monitoring.  From this
   perspective, the purpose of this document is to set principles of
   measurement in place that assure the quality of the other aspects of
   traffic engineering.

   The scope of this document is limited to those aspects of
   measurement pertaining to intra-domain operations, i.e., within a
   given autonomous system.  However, measurements on its boundary with
   other domains are included as well.  The focus is primarily on
   traffic engineering in Internet service provider environments.

   In this document, uses of traffic measurement in traffic
   characterization, network monitoring, and traffic control are first
   described.  Depending on the network operations to be performed in
   these tasks, three different time scales can be identified, ranging
   from months, through days or hours, to minutes or less.  To support
   these operations, traffic measurement must be able to capture
   accurately, within a given confidence interval, the traffic
   variations and peaks without degrading network performance and
   without generating an immense amount of data.  As one consequence of
   the need to avoid network performance degradation, specification of
   a suitable read-out period for each service class for traffic
   summarization is essential.  Other principles such as concise
   representation of measurements are identified as well.

   Traffic measurement can be performed on the basis of flows,
   interfaces, links, nodes, node-pairs, or paths.  Based on these
   objects, different measurement entities can be defined, such as
   traffic volume, average holding time, bandwidth availability,
   throughput, delay, delay variation, packet loss, and resource usage.
   Using these measured traffic data, in conjunction with other network
   data such as topological data and router configuration data, traffic
   matrix and other relevant statistics can be derived for traffic

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   engineering purposes.  Traffic measurement also plays a key role in
   network performance management.

   In addition to these capabilities, functions of a measurement system
   should also include data storage, data processing, statistics
   generation and reporting.  However, these aspects are outside the
   scope of this document.

   As a framework, this document is mainly concerned with a discussion
   of various technical issues surrounding traffic measurement,
   particularly in the area of statistical traffic load estimation for
   traffic engineering purposes.  As far as possible and to avoid
   duplication of effort, relevant work done in measurements by other
   standards organizations will be applied or adapted, and references
   to them will be made.  These include, in particular,

   . IP Performance Metrics (IPPM) Working Group of the IETF: its
     framework document [2] and the associated documents on individual
     metrics [3, 4, 5, 6, 7, 8, 9, 10]

   . ITU-T: Recommendation I.380/Y.1540 [11] and Recommendation Y.1541

4. Terminology

   The intent of this section is not to provide definition or
   description of terms used in this document.  Rather, it is to
   highlight the difference in usage of closely related terms.

4.1 Route, path

   A route is any unidirectional sequence of nodes and links, for
   sending packets from a source node to a destination node.  A path
   refers to an MPLS tunnel, i.e., a label-switched path [13].

   It should be pointed out that there are also methods for creating
   paths with other technologies such as frame relay or ATM.  The
   measurement described in this document may apply to these
   technologies with suitable adaptation.  To simplify description,
   reference is made to MPLS only in what follows.

4.2 Throughput, traffic volume

   Both quantities can be applied to a network, a network segment, or
   an individual network element.

   Throughput of a network, as a measure of delivered performance,
   refers to the maximum sustainable rate of transferring packets
   successfully across the network, under given network conditions,
   e.g., a given traffic mix, while meeting quality of service (QoS)
   objectives.  This usage is consistent with the definition of
   throughput for a network interconnect device as specified in [14].
   For real-time network control, active measurement of throughput by

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   probing may be used to determine the currently available capacity of
   a network to carry additional traffic.  (In an active measurement,
   test packets are injected into the network.  Data collected about
   these packets are taken as representative of the behavior of the

   Traffic volume, as a measure of the traffic carried, characterizes
   the level of traffic that a network is designed to support.
   Passive, i.e., in-service non-intrusive, measurement of the traffic
   volume is usually used to estimate the long-term offered traffic for
   the purposes of network dimensioning in the capacity-management and
   network-planning processes (see the Section on Time Scales for
   Network Operations).  A network should be properly dimensioned so
   that its throughput is adequate to handle the expected traffic

   Throughput is expressed in terms of number of data units per time
   unit.  Traffic volume is expressed in data units with reference to a
   read-out period (see the Section on Read-Out Periods).  For
   transmission systems, the data unit is usually a multiple of either
   bits or bytes.  For processing systems, the data unit is usually a
   multiple of packets.

5. Uses of Traffic Measurement

   Traffic measurement is used to collect traffic data for the
   following purposes:
   . Traffic characterization
   . Network monitoring
   . Traffic control

5.1 Traffic characterization

   . Identifying traffic patterns, particularly traffic peak patterns,
     and their variations in statistical analysis; this includes
     developing traffic profiles to capture daily, weekly, or seasonal
   . Determining traffic distributions in the network on the basis of
     flows, interfaces, links, nodes, node-pairs, paths, or
   . Estimation of the traffic load according to service classes in
     different routers and the network.
   . Observing trends for traffic growth and forecasting of traffic

   For example, traffic engineering measurements are usually used to
   determine the statistical moments of a traffic flow.  As suggested
   in [15], given the time series of packet arrivals, a suitable
   parametric stochastic model based on the mean and variance of the
   time series can be constructed.  This traffic model is then used in
   the ensuing phases of traffic engineering, such as link dimensioning
   to meet service objectives.

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5.2 Network monitoring

   . Determining the operational state of the network, including fault
   . Monitoring the continuity and quality of network services, to
     ensure that QoS/GoS objectives are met for various classes of
     traffic, to verify the performance of delivered services, or to
     serve as a means of sectionalizing performance issues seen by a
     customer.  [QoS reflects the performance perceivable by a user of
     a service, while GoS (grade of service) is used by a service
     provider for internal design and operation of a network.]
   . Evaluating the effectiveness of traffic engineering policies, or
     triggering certain policy-based actions (such as alarm generation,
     or path preemption) upon threshold crossing; this may be based on
     the use of performance history data.
   . Verifying peering agreements between service providers by
     monitoring/measuring the traffic flows over interconnecting links
     at border routers; this includes the estimation of inter- and
     intra-network traffic, as well as originating, terminating, and
     transit traffic that are being exchanged between peers.

   An example of using traffic measurements in this area might be
   monitoring packet loss rates at various points in a network to
   detect apparent link failure.  Another example is monitoring the QoS
   delivered to external peers by an autonomous system to ensure that
   peering agreements are met.

5.3 Traffic control

   . Adaptively optimizing network performance in response to network
     events, e.g., rerouting to work around congestion or failures.
   . Providing a feedback mechanism in the reverse flow messaging of
     RSVP-TE or CR-LDP signaling in MPLS to report on actual topology
     state information such as link bandwidth availability.
   . Support of measurement-based admission control, i.e., by
     predicting the future demands of the aggregate of existing flows
     so that admission decisions can be made on new flows.

   An example of traffic engineering measurements used to effect a
   traffic control mechanism is to configure policing mechanisms in
   response to traffic load and performance measurements.  A network
   operator could selectively throttle low-priority flows to improve
   near-real-time performance of higher-priority flows, and maintain
   tighter QoS envelopes.  Another example would be to use measurement
   results for feedback into IGP routing decisions, e.g., for adjusting
   the link weights based on them.

6. Time Scales for Network Operations

   The information collected by traffic measurement can be provided to
   the end user or application either in real time, or for record
   (i.e., data retention) in non-real time, depending on the activities
   to be performed and the network actions to be taken.  Traffic

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   control will generally require real-time information.  For network
   planning and capacity management as described below, information may
   be provided in non-real time after the processing of raw data.

   Broadly speaking, the following three time scales can be classified,
   according to the use of observed traffic information for network
   operations [15].

   Network planning
   Information that changes on the order of months is used to make
   traffic forecasts as a basis for network extensions and long-term
   network configuration.  That is, for planning the topology of the
   network, planning alternative routes to survive failures or
   determining where capacity must be augmented in advance of projected
   traffic growth.  Forecasting and planning may also lead to the
   introduction of new technology and architecture.

   Capacity management
   Information that changes on the order of days or hours is used to
   manage the deployed facilities, by taking appropriate maintenance or
   engineering actions to optimize utilization.  For example, new MPLS
   tunnels may be set up or existing tunnels modified while meeting
   service level agreements.  Also, load balancing may be performed, or
   traffic may be rerouted for re-optimization after a failure.

   Real-time network control
   Information that changes on the order of minutes or less is used to
   adapt to the current network conditions in near real time.  Thus, to
   combat localized congestion, traffic management actions may perform
   temporary rerouting to redistribute the load.  Upon detecting a
   failure, traffic may be diverted to pre-established, secondary
   routes until more optimized routes can be arranged.

7. Read-Out Periods

   A measurement infrastructure must be able to scale with the size and
   the speed of a network as it evolves.  Hence, it is important to
   minimize the amount of data to be collected, and to condense the
   collected data by periodic summarization.  This is to prevent
   network performance from being adversely affected by the
   unnecessarily excessive loading of router control processors, router
   memories, transmission facilities, and the administrative support
   systems.  For example, offline bulk file transfer may be used as a
   method to manage large volumes of measured traffic data.  Bulk
   transfer from routers to collection devices can help reduce the
   packet processing overhead experienced by using other management
   interfaces.  Also, data correlation or filtering rules may be set up
   to suppress redundant data, or to aggregate flows into suitable
   classes with the corresponding aggregation of statistics.  These
   types of data reduction may be used as an appropriate or acceptable
   means for pruning down the overall volume of traffic data that a TE
   system may ultimately have to store, maintain, and process.

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   A measurement interval is the time interval over which measurements
   are taken.  Some traffic data must be collected continuously, while
   others by sampling, or on a scheduled basis.  For example, peak
   loads and peak periods can be identified only by continuous
   measurement as traffic typically fluctuates irregularly during the
   whole day.  If traffic variations are regular and predictable, it
   may be possible to measure the expected normal load on pre-
   determined portions of the day.  This requires the definition of a
   busy period.  Special studies on selected segments of the network
   may be conducted on a scheduled basis.  Active measurement, with the
   involvement of network operator, may be activated manually.  For
   instance, active throughput measurement may be used to identify
   alternate routes during periods of network congestion.

   A measurement interval consists of a sequence of consecutive read-
   out periods.  Summarization is usually done by integrating the raw
   data over a pre-specified read-out period.  The granularity of this
   period must be suitably chosen.  It should be short enough to
   capture, with acceptable accuracy, the bursty nature of the traffic,
   i.e., the traffic variations and peaks.  Since measurements
   represent a load for the router, the read-out period should not be
   so short that router performance is degraded while a voluminous
   quantity of data is produced.   Also, read-out may be started when
   the measured data exceeds a preset threshold, or when the space
   allocated for temporarily holding the data in a router is exhausted.

   For a multi-service IP-based network, each service typically has its
   own traffic characteristics and performance objectives.  To ensure
   that service-specific features are reflected in the measurement
   process, different read-out periods may be needed for different
   classes of service.

8. Measurement Bases

   Measurements can be classified on the basis of where, and at which
   level the traffic data are gathered and aggregated.  This is similar
   to the concept of a *population of interest* as specified in ITU-T
   Recommendation I.380/Y.1540.  As defined therein, this refers to a
   set of packets, possibly relative to a particular pair of source and
   destination hosts, for the purposes of defining performance
   parameters.  However, measurement bases as used here may not have
   any association with a source-destination pair.

   In this document, customer-based measurements are not considered.
   Service providers will make decisions on how to perform the
   measurements needed, and there are various tradeoffs involved.  One
   option is to obtain the measurements directly from the network
   elements themselves, e.g., via SNMP (Simple Network Management
   Protocol).  Collecting the measurements on the operational network
   elements such as routers is sometimes a performance concern.
   Currently, there are a number of third-party measurement/monitoring
   products available.  Hence, another option is to deploy such

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   equipment, which might have performance advantages but also
   introduces additional cost.

   Regardless of the type of measurement source, either a network
   element or a third-party product, measurements should be collected,
   as far as possible, by a measurement source without requiring
   coordination with other measurement sources.  Thus, it is desirable
   to perform those measurements that do not require the use of
   specialized monitoring equipment connected to the network at
   multiple locations.  While each measurement source may act
   autonomously with regard to taking measurements, a network operator
   may specify some network-wide policy regarding measurement
   scheduling.  Such policy may be, say, the use of the same time of
   day, the same measurement interval, or measurement intervals that
   are multiples of each other (e.g., nested intervals with
   synchronized boundaries).  A schedule therefore should include such
   time information as the start, the duration, and periodicity of a
   certain measurement.

   The following measurement bases are considered in this document:
   . Flow-based
   . Interface-based, link-based, node-based
   . Node-pair-based
   . Path-based

8.1 Flow-based

   This is conceptually similar to the call detail record (CDR) in
   circuit-switched telecommunications networks.  It is primarily used
   on interfaces at access routers, edge routers, or aggregation
   routers where traffic originates or terminates, rather than on
   backbone routers in the core network.  Like CDR measurements, flow-
   based records are used to collect detailed information about a flow.
   This includes such information as source and destination IP
   addresses/port numbers, protocol, type of service, timestamps for
   the start and end of a flow, packet count, octet count, etc.

   As flow is a fine-grained object, measuring every flow that passes
   through all the edge devices may not be scalable or feasible.
   Hence, per-flow data are usually used in a special study conducted
   on a non-continuous schedule and on selected routers only.  Sampling
   of flow-based measurements may also be needed to reduce both the
   amount of data collected and the associated overhead.

8.2 Interface-based, link-based, node-based

   Passive measurement can be taken at each network element.  For
   example, SNMP uses passive monitoring to collect raw data on an
   interface at an edge or backbone router.  These data are stored in
   MIBs (Management Information Bases) and include counts on packets
   and octets sent/received, packet discards, errored packets.

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   To reduce the overhead in managing multiple links between the same
   ingress and egress points, there is proposal to aggregate links for
   network optimization [16].  Component links in such a *bundled link*
   will have same routing constraints, resource classes, and
   attributes.  Multiple links are treated as a single IP link.
   Traffic measurements, such as bandwidth availability, throughput,
   should consider the measurements for bundled links.  Also, such
   measurements should be protocol independent and media independent to
   ensure portability and commonality in the measurements.

8.3 Node-pair-based

   Active measurements by probing, as specified in the IPPM framework,
   can be conducted between each pair of major routing hubs for
   determining edge-to-edge performance of a core network.  This
   complements the passive measurements of the previous sub-section,
   which provide local views of the performance of individual network

   In telecommunications networks, each established call has an
   associated node-pair.  By maintaining a set of node-pair data
   registers (usage, peg count, overflow, etc) in each switch, node-
   pair-based measurements for traffic statistics such as the load
   between a given node pair are taken directly.  In contrast, in IP-
   based networks, currently such kind of node-pair-based measurements
   cannot be taken directly.  However, it is possible to infer them
   from flow-based passive measurements and other network information.
   A problem with this approach is that flow-based measurement data are
   voluminous.  Also, another problem that must be accounted for is the
   routing changes among the multiple routes due to, e.g., a change in
   the configuration of intradomain routing, or a change in interdomain
   policies made by another autonomous system.  This is further
   discussed in the Section on Traffic Matrix Statistics.

8.4 Path-based

   The ability of MPLS to use fixed preferred paths for routing
   traffic, so-called route pinning, gives the means to develop path-
   based measurements.  This may enable the development of
   methodologies for such functions as admission control and
   performance verification of delivered service.

   Like a flow, a path is associated with a pair of nodes.  However,
   path is a more coarse-grained object than flow, as paths are usually
   used to carry aggregated traffic.  In addition, when routing changes
   occur, the amount of traffic to be carried by a path will either not
   be affected or be merged with that of another path.  Because of
   these properties, path-based measurements are more scalable and may
   be used to provide more readily an accurate, network-wide, view of
   the traffic demands.  For example, the traffic between a given pair
   of nodes may be inferred from the aggregate of the traffic carried
   by the all the paths either terminated by or passed through the same

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9. Measurement Entities

   A measurement entity defines what is measured: it is a quantity for
   which data collection must be performed with a certain measurement.
   A measurement type can be specified by a (meaningful) combination of
   a measurement entity with the measurement basis described in the
   previous section.

9.1 Entities related to traffic and performance

   Some of the measurement entities listed below, such as throughput,
   delay, delay variation, and packet loss, are related to the
   respective IPPM performance metrics or the I.380/Y.1540 performance

   . Traffic volume (mean and variance, in number of bits, bytes, or
     packets transferred, as counted over a given time interval), on a
     per service class basis, at various aggregation levels (IP address
     prefix, interface, link, node, node-pair, path, network edge,
     customer, or autonomous system)
     Note:  (1) This is a measurement for the traffic carried by a
     network, a network segment, or an individual network element; it
     is used to derive the carried load or carried traffic intensity
     [17].  When measured during the busy period, this entity is
     normally used to estimate the traffic offered.  However, the
     estimation procedure should take into account such factors as
     congestion, which may result in decreased carried traffic.  In
     addition, congestion may lead to user behavior such as reattempt
     or abandonment, which may affect the actual traffic offered.  (2)
     To reduce uncertainty in traffic estimation, second-order measures
     may need to be developed.  (3) Measurement of traffic volumes over
     interconnecting links at border routers can be used to estimate
     the traffic exchange between peers for contract verification.

   . Average holding time (e.g., flow duration or lifetime, duration of
     an MPLS path), on a per service class basis
     Note:  (1) This is similar to call holding time in
     telecommunications networks.  Peg count, usage, and call holding
     time are three busy-hour entities that should be independently
     measured for both call-dependent and load-dependent engineering.
     This is important especially when the call busy hour and the load
     busy hour during a day are non-coincident, due to the hour-to-hour
     variation of call holding times.  (2) The holding time statistics
     of long-living static paths reflect the effect of network
     equipment failures, link outages, or scheduled maintenance, and
     hence may to used to derive information about up-time or service

   . Available bandwidth of a link or path - useful for load balancing,
     measurement-based admission control to determine the feasibility
     of creating a new MPLS tunnel (real-time information can be used
     for dynamic establishment)

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   . Throughput (in bits per second, bytes per second, or packets per
     Note:  (1) This is a measure of the "goodput."  That is, the rate
     at which a given amount of traffic excluding lost, misdelivered,
     or errored packets, that passes between a set of end points, where
     end points can be logically or physically defined.  The condition
     of the network, e.g., normal or high load, under which the
     measurement is taken should be noted.  (2) The protocol level at
     which a throughput measurement is taken must be specified, as the
     packet payload and packet overheads are protocol dependent.  (3)
     The average packet size may be inferred from the bit rate and
     packet rate measurements.  This quantity is useful to gauge router
     performance, since router operations are typically packet-oriented
     and small packets are more processing-intensive.

   . Delay (e.g., cross-router delay from node-based measurement may be
     used to measure queueing delay within a router; end-to-end one-way
     or round-trip packet delay can be obtained by node-pair-based
     Note: The condition of the network, e.g., normal or high load,
     under which the measurement is taken should be noted.  This is
     useful to determine if delay objectives are met.

   . Delay variation
     Note:  There are several methods to measure this quantity as
     specified in ITU-T and IPPM.  (1) In Appendix II of I.380/Y.1540,
     IP packet delay variation is defined via four alternative methods.
     The first two methods define an end-to-end two-point delay
     variation of a given packet, measured between two measurement
     points (such as ingress and egress), as the difference between the
     one-way delay of the given packet and some nominal delay.  This
     nominal delay is chosen to be the first packet delay in the first
     method and the average delay of the population of packets in the
     second method.  The third alternative, interval-based method,
     measures the percentage of packets with delay variations that fall
     outside some pre-specified delay variation interval.  Finally, the
     quantile-based method measures the distance (in time units)
     between pre-selected quantiles, e.g., 99.5 percentile and 0.5
     percentile, of the delay variation distribution.  This method is
     tighter than the interval-based method since it bounds the tail of
     the delay variation distribution.  In Y.1541, additional
     considerations and more alternatives of delay variations are
     described.  (2) In IPPM [8], the concept of a selection function
     is introduced that allows for the explicit designation of selected
     packets whose one-way delay values are compared to compute one-way
     delay variation.  For example, a selection function can be defined
     to select the consecutive packets within a specified interval, or
     to select the maximum and minimum one-way delays within a
     specified interval.

   . Packet loss

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     Note:  (1) While packet losses due to transmission and/or protocol
     errors may not be traffic related, unexpected excessive loss may
     be used as a means of fault detection.  (2) Packet losses due to
     policing or network congestion should be distinguished.  The
     former is a result of user violation of service contract and the
     network operator should not be penalized for it.  The latter,
     whether intentional or unintentional, is caused by network
     conditions such as buffer overflow, router forwarding process
     busy, and may not be the user's fault.  When policing is done by a
     network, measurement of non-conforming packets at the edge
     provides an indication on the extent to which the network is
     carrying this type of packets (which can potentially be dropped if
     network gets congested).  Loss due to congestion of any packets,
     including loss of non-conforming packets, is a useful measure in
     traffic engineering to account for resource management.  (3) Long-
     term averages can be measured by the I.380/Y.1540 IP packet loss
     ratio or by the IPPM Poisson sampling of one-way loss.  However,
     during the convergence times associated with routing updating, the
     loss may be high enough as to cause service unavailability.  This
     effect needs to be captured and statistics such as loss patterns,
     burst loss, or severe loss ratio may be useful.

   . Resource usage, such as link/router utilization, buffer occupancy
     (e.g., fraction of arriving packets finding the buffer above a
     given set of thresholds)
     Note:  (1) Depending on the architecture of a router, router
     utilization measurements may include processor and memory (e.g.,
     forwarding tables) utilization for each of the line cards and/or
     the central unit.  (2) Trigger points may be set when resource
     usage consistently exceeds a certain threshold.

9.2 Entities related to establishment of connection or path

   Where connection admission control is used, a measurement entity for
   monitoring network performance may be the proportion of connections
   denied admission.  Also, it may be useful to score the requested
   bandwidth within the traffic parameters for the setup request.
   Corresponding to the number of call attempts (i.e., peg count) in
   telecommunications networks, the number of connection requests, the
   number of flows, etc., may be measured in given read-out periods to
   characterize the traffic.

   To characterize paths, the following measurement entities may be
   defined: path setup delay, path setup error probability, path setup
   denial (blocking) probability, path release delay, path disconnect
   probability, path restoration time.

10. Measurement Types

   A measurement matrix can be defined wherein each column represents a
   measurement basis and each row represents a measurement entity.  An
   entry in this measurement matrix, corresponding to a meaningful and
   measurable combination of an entity and a basis, defines a

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   particular measurement type.  For each measurement type, there
   should be a set of measurement points specified to bound the network
   segment for the purposes of taking measurement.  A measurement point
   may be the physical boundary between a node and an adjacent link, or
   the logical interface between two protocol layers in a protocol

10.1 Measurement types related to traffic or performance

   The following measurement matrix illustrates some of the measurement
   types related to traffic or performance.  Potentially, there can be
   one such matrix for each service class.

            Bases:     Flow     Interface,   Node Pair      Path
  Entities:          (passive)   (passive)     (both)      (both)
  Traffic Volume        x(1)         x          x(3)        x(3)
  Avg. Hold. Time        x                                  x(3)
  Avail. Bandwidth                   x                      x(3)
  Throughput                                    x(4)        x(4)
  Delay                             x(2)        x(4)        x(4)
  Delay Variation                   x(2)        x(4)        x(4)
  Packet Loss                        x          x(5)        x(5)

   (1) This measurement type can be used to derive flow size
   (2) These are 1-point measurements.
   (3) As a starting point, statistics collected by passive measurement
   through the MIBs useful for traffic engineering [18, 19, 20] may be
   (4) Active measurements based on IPPM metrics are currently in use
   for node-pairs; they may be developed for paths.
   (5) Besides active measurements based on IPPM, path loss may
   possibly be inferred from the difference between ingress and egress
   traffic statistics at the two endpoints of a path.  However, such
   inference for the cumulative losses between a given node pair over
   multiple routes may be less useful, since different routes may have
   different loss characteristics.

10.2 Measurement types related to resource usage

   Another measurement matrix can be constructed for resource
   consumption.  This leads to a set of measurement types comprising
   the different usage, one for each network resource object such as
   router (processor and memory), link, and buffer, by different
   classes of traffic:

   . control (e.g., routing control) traffic
   . signaling traffic
   . user traffic from different service classes

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                        Bases:   Node     Link    Buffer
           Control Util.           x        x        x
           Signaling Util.         x        x        x
           Service Class Util.     x        x        x

   The amount of control and signaling traffic carried by a network is
   a function of many factors.  To name a few, they include the size
   and topology of the network, the control and signaling protocols
   used, the amount of user traffic carried, the number of failure
   events, etc.  Also, flooding of link-state advertisement (LSA)
   messages in Interior Gateway Protocol (IGP, such as OSPF or IS-IS)
   may cause significant routing control traffic during events such as
   an LSA storm as a result of failures due to fiber cuts or failed
   power supply.  The above utilization measurements for control and
   signaling traffic are intended to help develop guidelines for the
   proper dimensioning and apportionment of network resources so that a
   given level of user traffic can be adequately supported.  As the
   primary focus here is on user traffic measurements, the additional
   needs and properties of control and signaling traffic measurements
   are beyond the scope of this document.

11. Traffic Matrix Statistics

   An important set of data for traffic engineering is point-to-point
   or point-to-multipoint demands.  This data is needed in the
   provisioning of intradomain routes and external peering in the
   existing network, as well as planning for the placement and sizing
   of new links, routers, or peers.

   In current practice, estimates for traffic demands are usually
   determined from a combination of traffic projections, customer
   prescriptions, and service level agreements.  Under existing mode of
   operation, it is not easy to obtain network-wide traffic demands
   from the local interface measurements taken by different IP routers.
   As explained in [21, 22], information from diverse network
   measurements and various configuration files are needed to infer the
   traffic volume.  Besides raw measurement data, additional
   information such as topological data and router configuration data
   are required to obtain a network view.  Furthermore, destination-
   based routing/forwarding in IGP provides a network operator with
   primitive and limited control over the routing of traffic flows.
   This necessitates the association of a time sequence of forwarding
   tables from different routers to reconstruct the different routes
   used by the network over time. By using this auxiliary information,
   together with flow-based measurements, the above-cited references
   describe how to determine the traffic volume from an ingress link to
   a set of egress links by validating and joining various data sets

   Some shortcomings in today's method to derive traffic matrix
   statistics as above include the volume of data from flow-based

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   measurement, the lack of sufficient routing control information, and
   the need to correlate data from a variety of sources.  The routing
   control offered by MPLS can be used to avoid some of these
   deficiencies.  To take advantage of this capability, path-based
   passive measurement should be developed.  Furthermore, as explained
   in the Section on Path-based Measurement Bases, by aggregating the
   appropriate set of path-based traffic data, the corresponding node-
   pair-based traffic data can be obtained.  This will facilitate the
   derivation of traffic matrix statistics, possibly on a per service
   class basis.  Note that in the case of hop-by-hop routed label-
   switched paths that are established by Label Distribution Protocol
   (LDP) signaling, there is no explicit binding between path end
   points.  This will result in the use of different label bindings at
   both the ingress and egress nodes over time as network topology
   changes.  Although the forwarding equivalence class (FEC) to label
   binding information already exists in the MPLS FTN and LSR MIBs [23,
   18], a mechanism is needed to keep track of binding changes.  An
   example of such a mechanism may be the periodic exchange of FEC to
   label binding information for each ingress-egress pair.

   Besides traffic engineering, a major application of MPLS is the
   support of network-based virtual private networks (VPNs).  A VPN can
   be an enterprise network or a carrier's carrier network.  It is not
   the purpose of this document to discuss VPNs.  However, it is
   relevant to highlight the use of traffic measurements to maintain
   proper engineering and performance of MPLS tunnels in the support of
   VPNs between VPN sites.  This would include also the support for
   MPLS-based pseudo-wire connections as developed by the PWE3 Working
   Group [24].  For example, path-based measurement by a network
   operator on behalf of the VPN customers facilitates the estimation
   of the traffic offered by these VPNs.

12. Performance Monitoring

   General aspects of measurements required to support the operation,
   administration, and maintenance of a network are outside the scope
   of this document (see [25, 26, 27] for a discussion of MPLS OAM).
   The focus of the measurements here is only on operations related to
   traffic engineering and network performance management.

   A major component of performance management is performance
   monitoring, i.e., continuous real-time monitoring of the quality or
   health of the network and its various elements to ensure a
   sustained, uninterrupted delivery of quality service.  This requires
   the use of measurement, either passively or actively, to collect
   information about the operational state of the network and to track
   its performance.  For a discussion of passive monitoring and the use
   of synthetic traffic sources in active probing, see [28].  Alarms
   may be generated when the state of a network element exceeds
   prescribed thresholds.

   Performance degradation can occur as a result of routing
   instability, congestion, or failure of network components.  Periods

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   of congestion may be detected when the resource usage of a network
   segment consistently exceeds a certain threshold, or when the cross-
   router delay is unexpectedly high.  After the identification of a
   hot spot, active throughput measurement may be used to seek out
   alternate routes for congestion bypass.  Unexpected excessive loss
   of packets or throughput drops may be used as a means of fault
   detection, and may result in restoration activities.

   Internet utilities such as ping and traceroute have been useful to
   help diagnose network problems and performance debugging.  Utilities
   with similar functions would be essential for path-oriented
   operations like in MPLS.  This would include the capability to list,
   at any time, (1) for a given path, all the nodes traversed by it,
   and (2) for a given node, all the paths originating from it,
   transiting through it, and/or terminating on it.  A proposal for
   route tracing is described in [29].

13. Packet Sampling

   A wide spectrum of operational applications can be built on traffic
   measurement.  However, different applications usually require
   traffic measurements at different levels of temporal and spatial
   granularity.  To achieve an effective tradeoff between
   implementation complexity and the range of operational tasks to be
   enabled, a passive measurement framework based on packet sampling is
   proposed in [30].

   The use of packet sampling has two motivations.  First, the enormous
   volumes of traffic require that some form of data reduction to be
   used.  Second, simple data reduction by aggregation at the
   measurement point will not provide sufficiently detailed views for
   all network management applications or exploratory studies.  For
   this reason, packet sampling is proposed as a means to reduce data
   volume while still retaining representative detail.

   The primary aim of the proposal [30] is to define a minimal set of
   primitive packet selection operations out of which all sampling
   operations that are necessary to support measurement-based
   applications can be composed.  Operations currently under
   consideration include filtering and statistical sampling, and also
   hash-based packet selection, a method that can be used to support
   the determination of spatial traffic flows across a domain [31].
   Whichever method is used, the interpretation of the stream of
   measurements arising from sampled packets must be both transparent
   and standard.  Other goals are to specify a means to format and
   export measurements, and a means to manage the configuration of the
   sampling and export operations.

   The proposal positions these function to provide a basic packet
   sampled measurement service to higher level "consumers."  A typical
   consumer is a network management application that sits behind a
   remote measurement collector.  Such measurements can support
   applications for a number of tasks: troubleshooting, demand

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   characterization, scenario evaluation and what-ifs.  Another type of
   consumer is a higher level on-router measurement application.
   One potential class of examples is composite measurements (e.g.,
   interpacket delay statistics) formed from a number of individual
   packet measurements.  Another class is network security
   applications, e.g., IP traceback [32].  For some applications, the
   ability to have low latency between packet measurement and reporting
   will be particularly useful.

14. Statistical Estimation and Information Modeling

   This section deals with engineering methods in statistical
   estimation, as well as the need for an information model and
   associated repository schema for the measurements.

14.1 Engineering methods for statistical estimation of measures

   The use of the well-established methods of optimal estimation [33,
   34, 35, 36] to obtain estimates of the measures for TE is
   recommended.  This draws upon several facts:

   . Internet traffic is inherently band-limited, but non-stationary;
   . Internet traffic may be heavy-tailed and possess strong short-term
   . A stationary, band-limited process can be approximated arbitrarily
     closely by optimal estimation methods based on a finite number of
     past samples.

   Standard procedures for de-trending the raw data to provide "trend +
   stationary" decompositions should be adopted.  An example is the use
   of Autoregressive Integrated Moving Average (ARIMA) models, where
   first differences are applied to the raw (non-stationary) data,
   yielding a stationary derived process.  Then, the methods of optimal
   estimation can be applied in a practical setting (e.g., finite sample
   counts) to the derived stationary process to produce quality
   estimates of the measures defined herein.  As the original raw
   process may be any of the measurements discussed in this document,
   the above procedure may be applied without loss of generality to
   measures of delay, loss, or complex measures of network state such as
   path characteristics, etc.

   In addition, these methods need to be applied across multiple time-
   scales, so that TE applications can work with measures related to:
   . long-term trends over days, weeks, and months;
   . busy-hour characterizations; and
   . statistics and correlation properties on the order of seconds [37].

   The above estimation procedures apply equally to traffic workload,
   traffic performance, or other estimates of network state, such as the
   state of routes.

14.2 TE Measure Information Modeling

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   An information model is valuable for organizing data generated
   through the estimation process.  An information model is needed for
   TE measures because a complete model does not exist for these
   measures.  Measures must be associated with a large, and sometimes
   complicated set of attributes (e.g., as simple as an IP address of a
   measurement point, or as complex as the path of a round-trip

   Information models exist that richly describe network elements and
   their configuration [38].  These models have been extended to include
   policy mechanisms [39].  Specifications for flows have been developed
   for network resource allocation purposes [40].  No centralized
   information model exists that can completely describe many of the TE
   measures defined herein.  Therefore, necessary integrating
   information models that make maximal reuse of pre-existing work may
   need to be developed for TE measures.

   As a brief example of the limitations of existing information models,
   consider RFC 1363 [40] as a model for a traffic flow.  It can be
   described as collection of attributes defining traffic offered load,
   performance to be delivered (a goal), and the assurance level (risk)
   associated with the actual performance obtained.  The traffic offered
   load is specified via an envelope described by a token bucket concept
   (token bucket rate, bucket size) and a maximum transmission rate.

   This model, while clearly intended for description of what a network
   will tolerate of a flow, could also be used to describe a flow in a
   TE measure sense, e.g., "a flow that lives within the token rate x
   and size y with probability 0.999."  Note that a probability
   statement must be added to complete the characterization.  This type
   of specification is known as (sigma, rho) in the literature.  Also,
   note that adopting such an information model for flows lacks any
   flexibility to specify time scale, or more detailed second-order

   Similar limitations exist with respect to delivered performance
   specification in RFC1363, and the text of the RFC is quick to point
   out, for example, that the "loss model is crude."  For these reasons,
   and others, an appropriate information model is needed for TE
   measures that can support uniformity of data definition in subsequent
   TE applications.

   Several approaches and options for repository technology are now
   broadly discussed.  Relationships between TE measure information
   models on other information models (e.g., COPS) that drive network
   outcomes are of particular importance.

   Linkages may need to be considered between policy mechanisms and TE
   measures.  This is useful because, while policy-driven networking is
   well-developed between the policy repositories, policy control points
   and policy enforcement, policy content is very likely the output of
   TE applications.  Since TE applications are dependent upon TE

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   measures, it is advantageous to provide traceability between the
   measures and the engineering changes made as a consequence of them.

   Measures (represented by their estimates) should be centrally stored
   and collected in a logical sense.  This does not preclude distributed
   storage for purposes of volume management or security/survivability,
   but alludes to the need for a consistent retrieval mechanism (e.g.,
   NFS).  Two methods are: (1) extend MIBs with new definitions for TE
   measure estimates, and (2) create data depositories through more
   centralized facilities, such as LDAP repositories.  Both methods have
   merits as collection processes for TE measures, and are simple
   examples spanning a wide spectrum of solutions.  These two methods
   are discussed here for expository purposes, not to exclude other

   Using MIBs allows well-established SNMP protocol and related
   applications to retrieve data from the network elements being
   measured.  This is inherently "vendor-neutral," allowing commonly
   defined TE measurements to be stored for retrieval in a common MIB
   definition, regardless of network element vendor, technology or other
   differences.  Measurements from individual network elements
   (interfaces, routers, etc.) can be obtained "locally," if measures
   from a single network element are sufficient for a given TE
   application.  However, if a network-wide view of the measurements is
   desired, the drawback of a MIB-based approach is that the data must
   be retrieved from each element over the network.  As experience
   attests, this approach sometimes generates significant SNMP traffic,
   and during periods of high congestion (when measurements may be quite
   important) SNMP may not reliably fetch the measurement data. Finally,
   a MIB-based approach may be difficult to implement for various two-
   point measurements, such as end-to-end, or round-trip delay and delay
   variation.  Such measurements are not related to a single network
   element, and somewhat heuristic practices (e.g., storing end-to-end
   delay measurements in MIBs located on source address elements, etc.)
   are required.

   An LDAP repository approach centralizes the data storage.  This has
   the advantage that TE applications (such as offline and online TE, or
   measurement-based admission control) can be performed, and policy
   database content can be updated without invasive retrieval of data
   from network-wide MIBs.  Further, traceability can be established
   between the TE measurements in an LDAP repository, and the associated
   policy content derived from them.

   It is possible that both the MIB-based and LDAP-based (or another
   approach altogether) should be considered jointly.

15. Conclusions and Recommendations

   This document is intended as a framework for traffic metrics needed
   for successful TE.  Principles of best practice in traffic
   characterization and performance characterization are described.

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   For interoperable compatibility, basic areas of traffic measurement
   recommended for standardization include:

   (1) specific TE measurements

   . Use of node-pair-based traffic data to derive per-service-class
     traffic matrix statistics
   . Statistics of carried load versus performance
   . A standardized mechanism to detect and record label binding
     changes for LDP-signaled label-switched paths, to facilitate the
     collection of node-pair-based traffic data

   (2) traffic data collection methods

   . Need for uniform definitions across vendors and operators
   . Distinction between traffic offered load versus achieved
   . Need for higher-order statistics for service assurance
   . Need for packet-sampled measurements that preserve representative
     traffic detail at manageable sample volumes
   . Need for offline bulk file transfer and standardized
     filtering/aggregation mechanisms to manage large volumes of
     measured traffic data

16. Security Considerations

   The principles and concepts related to Internet traffic measurement
   as discussed in this document do not by themselves affect the
   security of the Internet.  However, it is assumed that any
   measurement systems that are developed or deployed by a service
   provider are responsible for providing sufficient data integrity and
   confidentiality.  It is also assumed that a service provider will
   take proper precautions to ensure that access to its measurement
   systems and all associated data is secure.  Methods to achieve these
   security considerations are not addressed in this document.

17. References

   Normative References

   References 1, 2, and 13 below are considered normative.

   Informative References

   1  D.O. Awduche, A. Chiu, A. Elwalid, I. Widjaja, and X. Xiao,
      "Overview and Principles of Internet Traffic Engineering," RFC
      3272, May 2002.
   2  V. Paxson, G. Almes, J. Mahdavi, and M. Mathis, "Framework for IP
      Performance Metrics," RFC 2330, May 1998.
   3  J. Mahdavi and V. Paxson, "IPPM Metrics for Measuring
      Connectivity," RFC 2678, September 1999.

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   4  G. Almes, S. Kalidindi, and M. Zekauskas, "A One-way Delay Metric
      for IPPM," RFC 2679, September 1999.
   5  G. Almes, S. Kalidindi, and M. Zekauskas, "A One-way Packet Loss
      Metric for IPPM," RFC 2680, September 1999.
   6  G. Almes, S. Kalidindi, and M. Zekauskas, "A Round-trip Delay
      Metric for IPPM," RFC 2681, September 1999.
   7  M. Mathis and M. Allman, "A Framework for Defining Empirical Bulk
      Transfer Capacity Metrics," RFC 3148, July 2001.
   8  R. Koodli and R. Ravikanth, "One-way Loss Pattern Sample
      Metrics," RFC 3357, August 2002.
   9  C. Demichelis and P. Chimento, "IP Packet Delay Variation Metric
      for IPPM," Internet-Draft, Work in Progress, August 2002.
   10 V. Raisanen, G. Grotefeld, and A. Morton, "Network performance
      measurement for periodic streams," Internet-Draft, Work in
      Progress, August 2002.
   11 ITU-T Recommendation I.380/Y.1540, "Internet Protocol Data
      Communication Service -- IP Packet Transfer and Availability
      Performance Parameters," February 1999.
   12 ITU-T Recommendation Y.1541, "Network Performance Objectives for
      IP-Based Services," May 2002.
   13 E. Rosen, A. Viswanathan, and R. Callon, "Multiprotocol Label
      Switching Architecture," RFC 3031, January 2001.
   14 S. Bradner (Editor), "Benchmarking Terminology for Network
      Interconnection Devices," RFC 1242, July 1991.
   15 G. Ash, "Traffic Engineering & QoS Methods for IP-, ATM-, & TDM-
      Based Multiservice Networks," Internet-Draft, Work in Progress,
      October 2001.
   16 K. Kompella, Y. Rekhter, and L. Berger, "Link Bundling in MPLS
      Traffic Engineering," Internet-Draft, Work in Progress, February
   17 W.S. Lai, "Traffic Measurement for Dimensioning and Control of IP
      Networks," Internet Performance and Control of Network Systems II
      Conference, SPIE Proceedings, Vol. 4523, Denver, Colorado, 21-22
      August 2001, pp. 359-367.
   18 C. Srinivasan, A. Viswanathan, and T.D. Nadeau, "Multiprotocol
      Label Switching (MPLS) Label Switch Router (LSR) Management
      Information Base," Internet-Draft, Work in Progress, January
   19 C. Srinivasan, A. Viswanathan, and T.D. Nadeau, "Multiprotocol
      Label Switching (MPLS) Traffic Engineering Management Information
      Base," Internet-Draft, Work in Progress, January 2002.
   20 K. Kompella, "A Traffic Engineering MIB," Internet-Draft, Work in
      Progress, September 2002.
   21 A. Feldmann, A. Greenberg, C. Lund, N. Reingold, J. Rexford, and
      F. True, "Deriving Traffic Demands for Operational IP Networks:
      Methodology and Experience," Proc. ACM SIGCOMM 2000, Stockholm,
   22 A. Feldmann, A. Greenberg, C. Lund, N. Reingold, and J. Rexford,
      "NetScope: Traffic Engineering for IP Networks," IEEE Network,
      March/April 2000.

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   23 T.D. Nadeau, C. Srinivasan, and A. Viswanathan, "Multiprotocol
      Label Switching (MPLS) FEC-To-NHLFE (FTN) Management Information
      Base," Internet-Draft, Work in Progress, January 2002.
   24 P. Pate, X. Xiao, T. So, A. Malis, T. Nadeau, S. Bryant, C.
      White, K. Kompella, and T. Johnson, "Framework for Pseudo Wire
      Emulation Edge-to-Edge (PWE3)," Internet-Draft, Work in Progress,
      June 2002.
   25 N. Harrison, P. Willis, S. Davari, E. Cuevas, B. Mack-Crane, E.
      Franze, H. Ohta, T. So, S. Goldfless, and F. Chen, "Requirements
      for OAM in MPLS Networks," Internet-Draft, Work in Progress, May
   26 ITU-T Draft Recommendation Y.1710, "Requirements for OAM
      Functionality for MPLS Networks," May 2001.
   27 ITU-T Draft Recommendation Y.1711, "OAM Mechanisms for MPLS
      Networks," May 2001.
   28 R.G. Cole, R. Dietz, C. Kalbfleisch, and D. Romascanu, "A
      Framework for Synthetic Sources for Performance Monitoring,"
      Internet-Draft, Work in Progress, May 2001.
   29 R. Bonica, K. Kompella, and D. Meyer, "Tracing Requirements for
      Generic Tunnels," Internet-Draft, Work in Progress, August 2002.
   30 N.G. Duffield (Editor), "A Framework for Passive Packet
      Measurement," Internet-Draft, Work in Progress, September 2002.
   31 N.G. Duffield and M. Grossglauser, "Trajectory Sampling for
      Direct Traffic Observation," IEEE/ACM Trans. on Networking, 9(3),
      pp. 280-292, June 2001.
   32 C. Partridge, C. Jones, D. Waitzman, and A. Snoeren, "New
      Protocols to Support Internet Traceback," Internet-Draft, Work in
      Progress, November 2001.
   33 S. Haykin, Ed., "Kalman Filtering and Neural Networks," Wiley
      Interscience, 2001.
   34 A. Papoulis, "Probability, Random Variables and Stochastic
      Processes," 3rd Ed., McGraw-Hill, 1991.
   35 A. Gelb, Ed., "Applied Optimal Estimation," MIT Press, 1974.
   36 I. R. Petersen, V. A. Ugrinovskii, A. V. Savkin, "Robust Control
      Design Using H<\infinity> Methods," Springer, 2000.
   37 V. Bolotin, J. Coombs-Reyes, D. Heyman, Y. Levy, and D. Liu, "IP
      Traffic Characterization for Planning and Control," Proc. ITC16,
      Edinburgh, Scotland, June 1999.
   38 Distributed Management Task Force (DMTF) Common Information Model
      (CIM), www.dmtf.org
   39 B. Moore, E. Ellesson, and J. Strassner, "Policy Core Information
      Model -- Version 1 Specification," RFC 3060, February 2001.
   40 C. Partridge, "A Proposed Flow Specification," RFC 1363,
      September 1992.

18. Intellectual Property Statement

   AT&T Corp. may own intellectual property applicable to packet
   sampling as presented in references [30, 31] and summarized in
   Section 13.  AT&T is currently reviewing its licensing intent
   relative to the Intellectual Property and will notify the IETF when
   AT&T has made a determination of that intent.

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19. Acknowledgments

   The support of Gerald Ash on this work and his comments are much
   appreciated.  Also, thanks to the inputs from Jim Boyle, Robert
   Cole, Enrique Cuevas, Alfred Morton, Thomas Nadeau, Moshe Segal,
   Bert Wijnen, and the Tequila project.  Nick Duffield contributed
   section 13 on packet sampling.

20. Author's Addresses

   Wai Sum Lai
   AT&T Labs
   Room D5-3D18
   200 Laurel Avenue
   Middletown, NJ 07748, USA
   Phone: +1 732-420-3712
   Email: wlai@att.com

   Blaine Christian
   Room D1-2-737
   22001 Loudoun County Parkway
   Ashburn, VA 20147, USA
   Phone: +1 703-206-5600
   Email: Blaine@uu.net

   Richard W. Tibbs
   Oak City Networks & Solutions
   P.O. Box 10292
   Raleigh, NC 27605, USA
   Phone: +1 919-510-9551
   Email: drtibbs@oakcitysolutions.com

   Steven Van den Berghe
   Ghent University/IMEC
   St. Pietersnieuwsstraat 41
   B-9000 Ghent, Belgium
   Phone: ++32 9 267 35 86
   E-mail: steven.vandenberghe@intec.rug.ac.be

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Internet-Draft  Framework for Internet Traffic Measurement    Sep 2002

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