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
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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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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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