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Internet Research Task Force                           W. Tavernier, Ed.
Internet-Draft                                   Ghent University - IBBT
Intended status: Informational                          D. Papadimitriou
Expires: July 19, 2011                               Alcatel-Lucent Bell
                                                                D. Colle
                                                 Ghent University - IBBT
                                                        January 15, 2011


    Learning Capable Communication Network (LCCN) problem statement
             draft-tavernier-irtf-lccn-problem-statement-01

Abstract

   Operational procedures and protocols of today's communication
   networks typically use explicitly defined mechanisms and
   representations to reach the goals associated to their design.  This
   practice results into numerous protocols having a restricted space
   for (self-)adaptability, flexibility, and sensitivity respective to
   their network context (e.g. network traffic conditions, failure
   conditions, etc).  On the other hand, a wide spectrum of learning and
   optimization techniques is available such that networks could learn
   and optimize their behavior in the running context.  This document
   describes the opportunities and challenges for a Learning Capable
   Communication Network (LCCN).

Status of this Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on July 19, 2011.

Copyright Notice

   Copyright (c) 2011 IETF Trust and the persons identified as the
   document authors.  All rights reserved.




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   This document is subject to BCP 78 and the IETF Trust's Legal
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   (http://trustee.ietf.org/license-info) in effect on the date of
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   described in the Simplified BSD License.


Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  3
   2.  Learning opportunities . . . . . . . . . . . . . . . . . . . .  4
     2.1.  Availability of network data and statistics  . . . . . . .  4
     2.2.  Availability of processing capacity  . . . . . . . . . . .  5
   3.  The learning process . . . . . . . . . . . . . . . . . . . . .  5
   4.  Architectural implications . . . . . . . . . . . . . . . . . .  7
     4.1.  From a pre-defined open-loop control towards a
           self-adaptive closed-loop control  . . . . . . . . . . . .  7
     4.2.  The integration of learning capability . . . . . . . . . .  9
     4.3.  Coexistance with current networking protocols,
           mechanisms and practices . . . . . . . . . . . . . . . . . 10
     4.4.  Complexity/control vs. performance/labour trade-off
           measurability  . . . . . . . . . . . . . . . . . . . . . . 10
   5.  Applicability  . . . . . . . . . . . . . . . . . . . . . . . . 11
     5.1.  Functional domains . . . . . . . . . . . . . . . . . . . . 11
     5.2.  Scope with respect to the hourglass model  . . . . . . . . 11
     5.3.  Existing work  . . . . . . . . . . . . . . . . . . . . . . 12
   6.  Research directions  . . . . . . . . . . . . . . . . . . . . . 13
     6.1.  Relation to existing research domains  . . . . . . . . . . 13
     6.2.  Experimental research objectives . . . . . . . . . . . . . 14
   7.  IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 14
   8.  Security Considerations  . . . . . . . . . . . . . . . . . . . 14
   9.  Conclusions  . . . . . . . . . . . . . . . . . . . . . . . . . 15
   10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 15
   11. Informative references . . . . . . . . . . . . . . . . . . . . 15
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 16












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1.  Introduction

   As currently instantiated, the Internet hour-glass model drives a
   top-down approach.  Current communication networks typically operate
   with an explicit internal representation of themselves, their network
   knowledge, and their global goals.  Routers follow explicitly
   (pre-)defined behavior, persistently decide and uniformly execute.
   Global Internet behavior is evaluated and configuration is when the
   evaluation indicates that the networking systems are not
   accomplishing what they were intended to, or when better
   functionality or performance is expected.

   In several Internet areas, this operational model shows its limits.
   Inter-domain routing protocols such as BGP are increasingly impacted
   by topology and policy dynamics, delaying their convergence due to
   inherent exploration properties.  Network management becomes more and
   more complex, as networks do not automatically take into account
   network traffic statistics and other dynamic properties.  Several
   efforts have been undertaken to overcome the increasing number of
   issues.  However, improvement of the routing system to accommodate
   various scales of challenges in network efficiency, further
   complicates its operation ([I-D.ietf-idr-bgp-issues]).  Further
   patching the inter-domain routing system and equipment will result
   into more operational complexity.

   In this document, we suggest an alternative (bottom-up) approach to
   the Internet routing and forwarding system operation.  Compared to
   current routed networks requiring explicit specification of expected
   behavior, self-adaptive systems could dynamically modify or adjust
   their behavior to varying network conditions in order to tune their
   operation, optimize their overall performance and even add
   functionalities through closed-loop adaptive control.

   We see three main drivers for the development of Learning Capable
   Communicatino Networks (LCCN): i) the availability of network-related
   data, ii) the wide range of possible learning paradigms that can be
   borrowed from domains such as Artificial Intelligence (AI), machine
   learning, and bio-inspired learning, and iii) the increased CPU
   capacity available at both forwarding and control plane level,
   allowing for background monitoring, learning and optimization in
   routers.

   The structure of this document is as follows.  In Section 2, we
   describe the opportunities for communication networks to learn how to
   improve their performance.  The next section (Section 3) gives a more
   formal but broad definition of the concept of learning.  Section 4
   provides a first set of architectural implications of adding learning
   capability to communication networks.  The applicability domain of



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   LCCNs is covered in Section 5, and possible research directions are
   described in Section 6.  Concluding remarks and future work are
   indicated in Section 9.


2.  Learning opportunities

2.1.  Availability of network data and statistics

   Hosts communicate by sending packets between each other via transit
   network nodes.  As such, a communication network is loaded with
   packets corresponding to network traffic flows between given network
   source and destination nodes.  Many techniques exist to gather
   statistics about the resulting traffic flows crossing routers.

   o  Online statistical counters measure properties of transiting
      traffic in a router using counters, for example the number of
      packets per destination prefix or used packet size distribution
      curves

   o  Traffic sampling: instead of counting certain traffic
      characteristics, unmodified traffic is captured for some time
      interval.  This sample is then used to derive certain
      characteristics, using e.g. the setting proposed in [Estan04]) by
      means of sample-and-hold technique.

   Unfortunately, the resulting statistical data is rarely used to
   directly improve the routing and/or forwarding decision of network
   nodes (referring to the active self-adaptive closed control loop in
   Section 4.1).  However, it is clear that network operation could
   benefit from taking these statistics automatically into account to
   allow for traffic spreading and network load balancing, ordering of
   prefix updates in traffic-informed re-routing decisions, and so on.
   To a lesser extend (since the routing system is deterministically
   adaptive to topological and/or policy changes), this observation also
   applies to routing information exchanges.

   Not only the statistics of network traffic are valuable but also the
   behavioral aspects of the network itself possibly contain usable
   information for increasing the performance of the network.
   Statistics about node or link failures can help network recovery
   mechanisms to fine tune their operation based on the specific
   statistical context of the running network.  Convergence behavior of
   routing protocols in the specific running context can be monitored
   such as to reduce the time of transient loops.  In brief, the
   specific running conditions of communication networks possibly hide
   (statistical) information, which are currently (largely) unused by
   current Internet protocols; nevertheless, providing an opportunity to



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   better analyze the behavior of the network behavior depending on the
   context it is running within.

2.2.  Availability of processing capacity

   The possibility of maintaining network statistics is not only
   dependent on the network conditions and environment themselves, but
   also on the physical feasibility of monitoring and storing them over
   longer periods.

   Supported by Moore's law, we observe that processing power is
   increasing over last years, either in pure clock frequency of CPU, or
   in the occurrence of combinations of multiple CPU's on one chip.  In
   combination with the high increase in line card speeds (up to 100
   Gbps), the possibility of capturing useful network statistics in
   background seems within reach.


3.  The learning process

   Many research fields study the concept of learning from various view
   points.  In the context of LCCNs, learning algorithms correspond to
   the (broad) class of algorithms that discover the relationship
   between system variables (i.e. input, output and hidden variables)
   from data samples of its environment (obtained by means of
   measurement/monitoring).  More formally, the learning process
   consists of the following steps (see Figure 1).
























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                             ,--.
                            +    +
                            |`--'|
                            |KIB |<------------------+
                            +    +                   |
                             `--'                    |
                               |                     |
                               v                     |
                        +----------------+           |
               ,--.     |    Learner     |        +------+
   E          +    +    |                |       /        \
   v          |`--'|    | +------------+ |      / Hypothe- \
   e -------->|    |------> Learning   | |----->\  sis h   /
   n      |   +    +    | |  algorithm | |       \        /
   t      |    `--'     | +------------+ |        +------+
          |  Training   +----------------+          | ^
          |  data set                               | |
          |                    +--------------------+ |
          |                    v                      +
          |    ,--.     +----------------+           /  \
          |   +    +    |   Performer    |          /    \
          |   |`--'|    | +------------+ |         / test \     target
          +-->|    |------> Learned    | |-------->\      /---> function
              +    +    | |  hypothesis| |          \    /
               `--'     | +------------+ |           \  /
               Test     +----------------+            +
             data set                                 ^
                |                                     |
                +-------------------------------------+

                                 Figure 1

   o  Step 0: Choose training and test data sets associated to a given
      (sequence of) event(s) observed in the system's running
      environment

   o  Step 1: Training (learner): learn an hypothesis h (model),
      function of the input (training data set) that approximates at
      best output y (symbolic = classification, numeric = regression).
      Knowledge: use prior "knowledge" stored in Knowledge Information
      Base (KIB) to learn h

   o  Step 2: Testing (performer): evaluate learned model using test
      data set







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4.  Architectural implications

   The control of dynamic systems such as communications networks and
   routers in particular, can be explained as an interative cycle
   referred to as the control loop.  The coming sections explain the
   difference of existing communications networks and routers, with the
   control loop of LCCNs.

4.1.  From a pre-defined open-loop control towards a self-adaptive
      closed-loop control

   The configuration and operation of existing communication networks
   typically consist of a set of components and algorithms acting in a
   relatively small space of states, transitions and optimization steps.
   Let's take as example routers: they distribute topology and/or
   distance information from which they compute (e.g. shortest) routing
   paths.  Using this information, they derive entries looked up to
   forward packets based on incoming packets' destination address.  When
   a topological or distance change occurs, routing updates are timely
   disseminated in the network such that each router achieves a coherent
   full view of the new network topology and/or distances and can re-
   compute new routing paths taking into account this new state of the
   network.  While these procedures might seem effective at first sight,
   they are mostly pre-determined and inflexible with respect to the
   environment they are running in.

   Indeed, routers are agnostic to traffic characteristics and to
   statistics of network failures.  This situation occurs because these
   techniques have been developed in the early days of packet
   communication networks.  At that time, computational and memory
   resources were scarce, and the resulting techniques needed to act
   sparingly with the available resources.  Moreover, most of these
   techniques aim to automate manual procedures used to configure or
   operate communication networks.  As such, routers forward packets
   based on their destination address by applying pre-determined
   decision rules and execution procedures.

   While many engineering disciplines, such as the automotive or bio-
   industry, have adopted learning techniques to improve the performance
   of their operational control loops, in computer networking, their
   application has been restricted mainly to passive applications
   leading to open-loop control procedures.  Examples of such
   applications are: time series models to analyze and predict network
   traffic data, anomaly detection techniques to check networks for
   strange events, or statistical models which try to detect Shared Risk
   Link Groups (SRLG).  Most of the applications of learning techniques
   are used as interesting side information in the context of network
   operation.  They help network managers to understand and predict the



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   behavior of their network; however few existing network operation
   models include this learning capability into their direct control
   loop.

   In this context, the overall objective is to bring the application of
   data mining and learning techniques one step further: towards the
   active integration of these techniques into the operational and
   control processes of communication networks.  For instance, we could
   augment the above control paradigm with a machine learning component
   enabling the system and network to learn about their own behavior and
   environment over time, to detect and analyze problems, adapt their
   decision, and tune their execution using output of models in order to
   increase their functionality and performance.  Systems with such an
   adaptive closed-loop control have network elements autonomously
   interrelated and controlled, dynamically adapting to changing
   environments, and learning desired behavior.  These fully distributed
   and technology-independent systems allow: i) self-configuration and
   self-organization, ii) self-protection and self-healing, and iii)
   self-optimization.  The objective is to improve the Internet control/
   routing and forwarding process by enabling, automating, and
   distributing the decision making processes involved in their
   operation.

                  +-----------+            +-----------+
    system   ==>  |  analyze  |----------->+  decide   | <==  rules
    knowledge     +-----------+            +-----------+
                        ^                        |
                        |                        v
       self-      +-----+-----+            +-----------+
     monitoring   |  detect   |<-----------+  execute  |      self-
                  +-----------+            +-----------+  configuration
                        ^                        |
                        |                        v
                     +------------------------------+
                     |      Controlled Element      |
                     +------------------------------+

                                 Figure 2

   Using a more advanced control loop, the routing systems locally learn
   from network traffic, failure patterns and other context-related data
   observed in the network, and locally adapt their procedures to
   optimize their decisions depending on the running context and their
   internal state.  The resulting self-adaptive closed-loop control is a
   four step cyclic process consisting of: i) a detection phase (e.g.,
   monitor network traffic) which is about monitoring data, ii) an
   analysis or learning phase (e.g., build traffic models for
   prediction) in which the data obtained during the detection phase is



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   analyzed and upon which models can be learned, iii) infer rules/
   decisions from the performed/learned analysis such that the learned
   model can influence the operation of the network and iv) an execution
   phase.

4.2.  The integration of learning capability

   While it is premature (and part of the research work) to detail the
   implications on the Internet architecture, the design of a control
   system incorporating learning capability would benefit from the
   following design principles.

   o  Adaptability: modular instead of relying on unified and monolythic
      approach in order to ensure gradual development (e.g. access vs
      core router)

   o  Segmentability: rely on relative local view rather than a network
      global view in order to ensure scalability, robustness, and
      resiliency

   o  Sizeability: inherits distributed properties and capabilities of
      routing system (e.g. intra- vs inter-domain) in order to ensure
      organic deployment --instead of a uniform and ubiquitous plane
      construction

   Taking these principles into account, the resulting architecture
   should specify: i) expected behavior of the self-adaptive closed-loop
   process, ii) its components, and iii) the interfaces with existing
   routers' components and between learning-capable routers of a network
   (both intra- and inter-domain).  The resulting closed-loop adaptive
   control includes a learning component that is either an upfront step
   or an online process, a feedback phase, and interactions with router/
   network control.

         Today                  Step 1                    Step 2
    +--------------+      +----------------+       +------------------+
    |              |      | +------------+ |       | +--------------+ |
    | +----------+ |      | |  Learning  | |       | |   Routing    | |
    | | Routing  | |      | +------------+ |       | |  + learning  | |
    | +----------+ |      |  weak coupling |       | +--------------+ |
    |              | ==>  | +------------+ |  ==>  |    integrated    |
    |              |      | |   Routing  | |       |  strong coupling |
    | +----------+ |      | +------------+ |       | +--------------+ |
    | |Forwarding| |      | +------------+ |       | |  Forwarding  | |
    | +----------+ |      | | Forwarding | |       | |  + learning  | |
    |              |      | +------------+ |       | +--------------+ |
    +--------------+      +----------------+       +------------------+




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                                 Figure 3

   Including learning capabilities into current Internet router
   architectures can follow a phased approach.  Internet routers
   typically consist of two functional components: i) a forwarding
   component which takes care of processing and forwarding packets
   according to pre-configured forwarding tables, and ii) a routing
   component which takes care of distributing topology/distance
   information, computing (shortest) routing paths using this
   information, and storing resulting entries into routing tables.
   Forwarding table entries are subsequently derived from routing table
   entries.  As a first integration step, a new functional component
   comprising learning capability could be included.  The new component
   would then be weakly coupled to the existing forwarding and routing
   components.  This implies that the routing and/or forwarding
   component can be enhanced by of the learning component.  These
   functionalities could be called via pre-defined interfaces between
   the components.  While this is an overlaid but modular build-up of a
   router, integration of learning capability can go one step further.
   Indeed, in a next phase, instead of a separate learning component,
   the learning functionality could be tightly integrated into the
   routing and forwarding components themselves.  This implies that the
   routing and forwarding processes themselves comprise a learning cycle
   (a self-adaptive closed-loop control).  It is clear that both the
   phasing and the detailed specification of the architecture is an
   important challenge in the design of LCCNs.

4.3.  Coexistance with current networking protocols, mechanisms and
      practices

   The roll-out of learning capability into communication networks
   preferrably allows to coexist with well-functioning existing network
   protocols and mechanisms.  This means that LCCNs should not enforce
   the networking environment to use them or adapt to them, even though
   they could improve the resulting network performance or solve a
   number of issues.  As such, a transition path towards communication
   networks including more learning-capability becomes possible without
   introducing abrupt transition paths.

4.4.  Complexity/control vs. performance/labour trade-off measurability

   The implications of using LCCNs should be addressed by determining
   the relative complexity and understandability they introduce.  This
   does not mean that complex (or black box) LCCN approaches are out of
   scope, it implies that the additional complexity and
   understandability resulting from the introduction of this control
   component should be measurable or can be at least characterized.
   Measurability (and associated metrics) is an integral part of the



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   investigation work.  The assesment should allow users of LCCNs to
   decide on the level of control vs. performance they are willing to
   give up/gain.  In this context, the analogy can be made with manual
   configuration of static routing tables vs. running automated shortest
   path protocols.  It is clear that a certain level of control is given
   up by allowing automated routing protocols to configure routing
   tables.  However, the resulting configuration is verifyable (by
   routing table inspection), the used algorithm (e.g.  Dijkstra
   shortest path calculation) is known, and the resulting reduction in
   manual intervention is clear.  On the other hand, the more laborous
   manual configuration allows for setups that are sometimes more tuned
   to specific traffic patterns (e.g. avoiding bottlenecks) than
   shortest path-protocols.  In most scenario's, the trade-off is clear
   for network operators: larger networks typically use automated
   routing protocols for the population of routing tables, whereas
   smaller, specialised network setups sometimes result into manually
   configured routing tables.  A similar type of trade-off is desired
   for LCCNs.


5.  Applicability

5.1.  Functional domains

   The incorporation of learning component within the router
   architecture aims to i) enhance Internet functionality in order to
   cope with known operational challenges such as manageability, and
   diagnosability, ii) address new challenges such as security and
   accountability, and iii) improve its performance (in terms of e.g.
   scalability and availability) by adapting forwarding and routing
   system decisions.  In this context of network quality, we can think
   of the automated inclusion of network traffic knowledge into the
   configuration of routes and resulting forwarding tables.

5.2.  Scope with respect to the hourglass model

   Even if learning paradigms can be applied at all levels of the hour-
   glass model, LCCN-related research focuses on the (largest) lower
   half of the hourglass model ("everything over IP, and IP over
   everything").  As depicted in Figure 4, the goal of LCCN research is
   to apply learning capabilities from the transport layer up to the
   physical layer (including thus also the network and datalink layers).

   Whereas learning capability is typically being used at the
   application layer already, for example by banking applications,
   large-scale websites such as Amazon or Google, except for TCP, the
   real networking machinery that is running below is still relying on
   low-information processes with very limited learning capabilities.



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   The incorporation of a learning component within wired and wireless
   communication network systems aims to improve both their operation
   and performance from the physical network layer up to the TCP/IP
   layer.  TCP can be qualified as an exception in the sense that it
   incorporates some of the procedures involved in learning processes.
   Indeed, its transmission window size is adaptively changed during the
   communication between network end points such as to maximize
   throughput while keeping the resulting congestion as low as possible.
   However, it mainly concerns end-to-end learning while learning within
   the network itself provides additional value (as shown by the work
   performed e.g. in [Tavernier10]).

               +---------------------+
                \  email, WWW,      /
                 \    TV, ...      /
                  \---------------/
                   \SMTP,HTTP,RTP/
            ---     \-----------/     ---
             ^       \ TCP,    /       ^
             |        \   UDP /        |
             |         \-----/         |
      LCCN   |         / IP  \         |
      scope  |        /-------\        |
             |       /Ethernet,\       |
             |      /  PPP,...  \      |
             |     /-------------\     |
             v    / CSMA, Sonet   \    v
            ---  /-----------------\  ---
                /copper,fiber,radio \
               +---------------------+


                                 Figure 4

5.3.  Existing work

   Although the penetration of learning capability in current network
   protocols is rather low, in several domains some studies have been
   conducted on the possible value of introducing learning capability or
   intelligence into the networking mechanisms.

   Learning systems have been succesful applied for example in cognitive
   radio networks and optical networks.  Using such systems, wireless
   network nodes adaptively change their transmission and/or reception
   parameters to communicate efficiently avoiding interference with
   other networks and nodes.  The adaptive change of these parameters is
   based on the active monitoring of several factors in the external and
   internal radio environment, such as radio frequency spectrum, user



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   behavior and network state.  More information about cognitive radio
   networks can be found in [Haykin2007].

   [Riziotis07] made a survey on the succesful application of
   computational intelligence techniques in the domain of photonics and
   optical networks.  Tens of studies are cited on the succesfull
   application of optimization and learning techniques in the design and
   operation of optical networks.  For example in [Goncalves04], agents
   make use of Artificial Neural Networks for monitoring an optical link
   of a network and predicting anomalous situations so that pro-active
   measures can be taken before faults occur.  This technique showed to
   be significantly less costly compared to providing 1+1 protection on
   DWDM links.

   The insight resulting of bringing together conducted research on
   learning capability in networked environments can result into a
   common base of and architecture to further investigate and deploy
   learning capability into new networked contexts.  Such a bottom-up
   approach can be valuable as it can give us lessons in common
   challenges, and ways to tackle them in order to reach a higher level
   of adoption of LCCNs.


6.  Research directions

6.1.  Relation to existing research domains

   Learning opportunities in communication networks have characteristics
   that are typical well-suited for research techniques borrowing from
   (machine) learning, robotics, AI, computational biology, etc.

   o  Difficult to explicitly characterize: events cannot be well
      characterized even when examples are available (inherent
      complexity in characterizing an event)

   o  Correlation: hidden correlations and trends between events within
      large amounts of associated data

   o  Dynamicity: changing conditions over time (in particular, for
      routing system but also variability of traffic, user expectations
      and behaviors)

   o  Quantity: amount of available data is too large for handling by
      manual intervention

   o  Evolutive: new events are constantly detected/discovered





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6.2.  Experimental research objectives

   Experimental research is a primary goal of the activities to be
   conducted.  The following objectives would be targeted:

   o  The production of various studies is stimulated and should enable
      evaluation of performance and functional improvement resulting
      from the exploitation of various learning paradigms.  A common
      understanding of these paradigms and their associated capabilities
      could complement this first step.  The resulting bottom-up
      approach allows to combine insights of several use cases involving
      learning in networks to find the common base and best
      architecture/practices in the development of LCCNs.

   o  As different distribution models can be considered for what
      concerns the distribution of the learning processes (taking into
      account the various objectives but also constraints resulting from
      network partition), determining which model best fit Internet
      evolution is a specific target of this research activity.

   o  Iterative cycles of experimentation shall allow to determine
      suitability of the resulting architecture as well as to determine
      practical feasibility, applicability and deployability of the
      concept on a large scale.  Documentation of appropriate use cases/
      scenarios would complement this work item.


7.  IANA Considerations

   This memo includes no request to IANA.


8.  Security Considerations

   It is desirable that LCCNs provide visibility on the possible mis-use
   of their learning capability.  As such, the assesment of their
   attractiveness for deployability becomes easier.

   Beside the research objectives detailed here above, security
   mechanisms for "communication channels" between learning components
   and "learning components" themselves shall be considered comprising
   among others message authentication but also means to prevent e.g.
   man-in-the-middle and DDoS attacks.  In the LCCN context, the
   question becomes what is sufficient for protecting the Internet
   against such attacks.  Is it sufficient to provide secure
   communication channels as well as adequate authentication and
   verification/validation mechanisms for the information exchanged over
   these channels, or can we rely on learning to determine protecting



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   decisions systems should take to ensure their own defense against
   such attacks ?  These are security topics that can be further
   investigated in the context of LCCN research.


9.  Conclusions

   Current communication networks fail to use network-related statistics
   which could be valuable to improve their performance.  In addition,
   current networks fail to provide solutions to challenging issues,
   because they become too complex to operate and manage by manual/open
   loop procedures.  A learning-capable communication network (LCCN)
   includes a learning component which learns based on the network
   environment statistics and adapts and optimizes its behavior upon
   this.  This gives new possibilities to improve network efficiency in
   several domains including network recoverability, accountability,
   security, scalability, and so on.  The challenge (and next steps) of
   LCCNs lies into: i) developing self-adaptive closed)loop control
   system relying on learning capability, ii) building and applying it
   to various network mechanisms and iii) verifying the resulting
   prototypes in experimental environments.


10.  Acknowledgements

   This work is supported by the European Commission (EC) Seventh
   Framework Programme (FP7) ECODE project (Grant No.223936).


11.  Informative references

   [AI-modern]
              Russell, S., "Artificial Intelligence: A Modern Approach",
              2003.

   [Estan04]  Estan, C., "Building a better NetFlow", october 2004.

   [Goncalves04]
              Goncalves, C., "Applying artificial neural networks for
              fault prediction in optical network links", december 2007.

   [Haykin2007]
              Haykin, S., "Cognitive radio: brain-empowered wireless
              communications", february 2007.

   [I-D.ietf-idr-bgp-issues]
              Lange, A., "Issues in Revising BGP-4 (RFC1771 to
              RFC4271)", draft-ietf-idr-bgp-issues-03 (work in



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              progress), August 2010.

   [PRML]     Bishop, C., "Pattern Recognition and Machine Learning",
              october 2003.

   [Riziotis07]
              Riziotis, C., "Computational intelligence in photonics
              technology and optical networks: A survey and future
              perspectives", december 2007.

   [Tavernier10]
              Tavernier, W., "Using AR(I)MA-GARCH models for improving
              the IP routing table update", october 2010.


Authors' Addresses

   Wouter Tavernier (editor)
   Ghent University - IBBT
   Gaston Crommenlaan 8 bus 201
   Gent,   9050
   Belgium

   Phone: +32(0)9 331 49 81
   Email: wouter.tavernier@intec.ugent.be


   Dimitri Papadimitriou
   Alcatel-Lucent Bell
   Copernicuslaan 50
   Antwerpen,   2018
   Belgium

   Phone:
   Email: dimitri.papadimitriou@alcatel-lucent.com


   Didier Colle
   Ghent University - IBBT
   Gaston Crommenlaan 8 bus 201
   Gent,   9050
   Belgium

   Phone: +32(0)9 331 49 70
   Email: didier.colle@intec.ugent.be






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