Cognitive augmented routing system and its standardisation path All Rights Reserved © Alcatel-Lucent 2010, ##### Dimitri Papadimitriou, Bernard Sales Alcatel-Lucent, Bell Labs March, 2010 ETSI Future Network Technologies Workshop
Cognitive augmented routing system
and its standardisation path
All Rights Reserved © Alcatel-Lucent 2010, #####
Dimitri Papadimitriou, Bernard Sales
Alcatel-Lucent, Bell Labs
March, 2010
ETSI Future Network Technologies Workshop
Self-Adaptive (top-down) vs Self-Organizing (bottom-up)
Current Internet model -> top-down approach
� Evaluate global behavior and change it when the evaluation indicates that
networking systems are not accomplishing what they were intended to do, or
when better functionality or performance is possible
� Typically operate with an explicit internal representation of themselves and their
global goals
Patching is reaching its limits
Improvement of the routing system to accommodate various scales of
All Rights Reserved © Alcatel-Lucent 2010, #####2 |Cognitive augmented routing system and its standardisation path | March 2010
� Improvement of the routing system to accommodate various scales of
challenges in network efficiency further complicates its operations
-> Further patching of the routing system and equipment will create more
operational complexity
Alternative: learning and reasoning
� Systems dynamically adapt their behaviour to varying network conditions
in order to monitor the operation, optimize overall performance and
even add functionalities
-> Bottom-up approach (with rules translating high-level to low-level objectives)
Why Learning Paradigm for the routing system
Machine learning
� Class of algorithms that discover the relationship between the variables of a system
(i.e. the input, output and hidden variables) from direct samples of the system
� Example of field of applications
� Natural language processing, speech and handwriting recognition
� Object recognition in computer vision, pattern recognition
� Bioinformatics (classifying DNA sequences)
Events characterising networking problems are similar to the ones classically
All Rights Reserved © Alcatel-Lucent 2010, #####3 |Cognitive augmented routing system and its standardisation path | March 2010
Events characterising networking problems are similar to the ones classically
addressed by machine learning
� Nature: events cannot be well characterized even when examples are available (inherent complexity in characterizing an event)
� Relationship: hidden correlations and trends between events within large amounts of associated data
� Environment: changing conditions over time (in particular, for routing system but also variability of traffic, user expectations & behaviors)
� Quantity: amount of available data is too large for handling by manual intervention
� Evolutivity: new events are constantly detected/discovered
Architectural Principles
� Adaptability: modular instead of relying on unified and ubiquitous approach to ensure gradual development (e.g. access vs core router)
� Segmentation: rely on relative local view rather than a network global view to ensure scalability, robustness, and resiliency
r5
r2
r3r4
r1
r5
r2
r3r4
r1 No !
r5
r2
r3r4
r1
r5
r2
r3r4
r1 No !
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� Sizeability: inherits distributed properties and capabilities of routing system (e.g. intra- vs inter-domain) instead of a uniform and ubiquitous plane construction to
ensure organic deployability
r3r4 r3r4r3r4 r3r4
r4
r7
r3
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r5
Inter-domain Intra-domain
r2
r1 Uniform
r1
r2
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r5
No !r4
r7
r3
r6
r5
Inter-domain Intra-domain
r2
r1 Uniform
r1
r2
r3
r4
r5
No !
Driving Concept
Augment control paradigm of lower-level data collection and decision making
process, with machine learning component enabling system and network to
� Learn about its own behavior and environment over time
� Detect and analyze problems, tune its operation and increase its functionality and performance
Cognitive engine using semi-supervised, online, distributed machine learning
Router with Machine Learning Engine
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Current router design
Routing Engine
Forwarding Engine
Control
Data
Packet Packet
Routing infoRIB
FIB
Routing
Engine
Forwarding Engine
Control
Data
Machine Learning Engine
data decisions
RIB
FIB
Learned rules
and decisions
Routing info Routing info
Packet
KIB
LearnerTraining data set
Learning algorithm
Hypothesis hf ∈∈∈∈ H
rule (informative or predictive) + error (= training error)
Exp. E
Knowledge Information Base (KIB): stores prior knowledge ≡≡≡≡ learned rules
Test Performer
KIB
[x ; y]
hf
Improve hf
Learning System
H space
hf(x): approx of f | y = f(x)
y = hf(x)
All Rights Reserved © Alcatel-Lucent 2010, #####6 |Cognitive augmented routing system and its standardisation path | March 2010
Test data set
Performer
Learned hyp (h f)
[x' ; -] [x' ; y' = hf(x’)]
[x' ; y']
Improve hf
Step 0: Choose training experience E as well as training and test data sets for experience E
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 KIB to learn h
Step 2: Testing (performer): evaluate learned model using test data set
Test h ff target function
( ≡ learned model)
Example: SRG Inference
Current Link-state routing Link-state Routing ⊗⊗⊗⊗ Machine Learning
4
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2
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1
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Failure
4
8
3
7
2
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1
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Failure
SRGSRG
SRGSRG
link [1,2] and [7,8] share the same risk
(belong to the same SRG)
Infer Shared Risk Group (SRG) from LSA arrival pattern to prevent successive SPT re-
computation upon shared-risk failure (affects multiple links simultaneously)
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4
8
3
7
2
6
1
5
First SPT Recomputation
LSA(1,2)
4
8
3
7
2
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1
5
Second SPT Recomputation
LSA(7,8)
LSA(7,8)
Failure
Failure
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8
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2
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1
5
Single SPT Recomputation taking into account both link failures
LSA(1,2)LSA(7,8)
Failure
Failure
time
LSA(1,2) LSA(7,8)
receive predict
LSA
ECODE Project (Experimental COgnitive Distributed Engine)
http://www.ecode-project.eu/
STREP Project part of Future Internet Research and Experimentation (FIRE) Cross
competences / skills and Consortium
Networking Machine Learning
"The overall objective of the ECODE project is to investigate a newarchitectural component to sustaingrowth of an Internet that performs in accordance to what it is expected to deliver to end-users. With this improvement that preserves its
All Rights Reserved © Alcatel-Lucent 2010, #####8 |Cognitive augmented routing system and its standardisation path | March 2010
Experimentation
improvement that preserves its original design principles, the Internet is also expected to perform according to the end-user expectations while coping with a growing number of userswith increasing heterogeneity in applicative communication needs.
This experimentally driven research project will determine whether composing the Internet high-level goal - societal, economical, etc. - can be translated into lower-level objectives (in terms of functionality and performance) and constraints (both technical and non-technical) and enforced via this novel cognitive component."
Cognitive augmented routing system –-
standardisation approach
All Rights Reserved © Alcatel-Lucent 2010, #####
ETSI Future Network Technologies Workshop
Traditional standardisation process not necessarily well adapted for research
Traditional standardisation process
� Short term agenda
� Focus on current work
items/roadmap
� Generally solution oriented
� Tactics
� Regulation
Requirements from research on standardisation models
� Pre-standardisation work
� More lightweight
� Open to academic participants
� Should allow a design based on the iterative/“spiral” model
VS
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Standardisation bodies adapting their processes to capture these requirements
� IRTF/ISOC, ETSI ISG, ITU-T Focus Group, IEEE-SA Industry Connections Program
� It is also possible to incubate research ideas with the traditional standardisation process
� Generally leads to the creation of dedicated Working Group with requirements/use cases and architecture definition in scope
Set the standardisation approach for research projects
Needs a well-defined methodology
� What do we need to standardise to allow the technology proposed by the
project to be interoperable / deployable at a large scale
� Role and impacts of standardisation bodies on the segment targeted by
the research project
� Standardisation activities is a food chain model
� Do we need to improve the standardisation eco-system to maximise the
1
2
3
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Do we need to improve the standardisation eco-system to maximise the
chance of success
� Create new Technical Committee, working groups and/or
� Attract major actors
� Identify the “structuring” aspects when choosing standardisation bodies
Derive a coherent standardisation approach
4
Systematic approach for standards – Cognitive augmented routing as a use case
� The functional and network architecture
components of the proposed solution.
� The communication protocols between
different cognition augmented IP routers
(i.e. between two cognitive engines)
� The additional interfaces between the
cognitive engine and the (existing)
components of the control engine and the
forwarding engine of IP routers
Routing
Engine
Forwarding Engine
Control
Data
Machine Learning Engine
data decisions
RIB
FIB
Router with Machine Learning Engine
Learned rules
and decisions
Routing info
Packet
KIB
Routing
Engine
Forwarding Engine
Control
Data
Machine Learning Engine
data decisions
RIB
FIB
Router with Machine Learning Engine
Learned rules
and decisions
Routing info
Packet
KIB
1 What do we need to standardise?
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forwarding engine of IP routers
Role/impacts of standardisation
bodies and forum
IRTF,
ITU-T, ETSI
IRTF,
ITU-T, ETSI
IRTF
IETF (IRTF)
None
Marketing
Needs Architecture Solutions Test/Interop
2 3 Do we need to improve the
standardisation eco-system?
Structuring aspects of the research approach and target to standardisation
Introduction of machine learning component to address
� Current operational challenges
� Limit infrastructure and operational complexity resulting from Internet growth (compared to continuous patching existing routing
equipment) → Ensure Internet durability
� Reduce/Improve performance degradation/gain
Protocols
4 Structuring aspects of the research approach and target to standardisation
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� Reduce/Improve performance degradation/gain by adapting forwarding and routing decisions
� New challenges to meet Future Internet requirements
� Extend Internet functionality (diagnosability, security, etc.) to cope with known Internet challenges without impacting its genericity and evolvability
Iterative cycles of experimental research for progressive validation and adaptation (to cope with discovered limitations)
De-risk
Validate
Advanced
architecture
Proposed standardisation approach for the Cognitive augmented routing
1) define problem statement
2) Translate/use initial results into specific research goals and delimit the topic and scope of research
3) Build-up the community of interests
4) Define/refine architecture framework, design goal and protocol framework
5) Experiment distribution of information between Machine Learning Engines
6) Formulate recommendations
Socialise
De-risk
Validate
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6) Formulate recommendations
7) Start base protocol engineering work
7’) Start advanced architecture work
IRTF IRTF
ETSI
IRTF
IETF
(IRTF)
First, iterate in IRTF
Specify next
Specify
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