Cognitive augmented routing system and its standardisation path

Transcription

1 Cognitive augmented routing system and its standardisation path ETSI Future Network Technologies Workshop Dimitri Papadimitriou, Bernard Sales Alcatel-Lucent, Bell Labs March, 2010

2 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 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) 2 Cognitive augmented routing system and its standardisation path March 2010

3 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 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 3 Cognitive augmented routing system and its standardisation path March 2010

4 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 r1 r2 r1 r2 No! r5 r5 r4 r3 r4 r3 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 r1 r3 r5 Uniform r2 r4 r7 r6 r1 r2 r3 r4 r5 No! Inter-domain Intra-domain 4 Cognitive augmented routing system and its standardisation path March 2010

5 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 Current router design Learned rules and decisions Machine Learning KIB Control Routing info Routing RIB Control Routing info Routing info data Routing decisions RIB Packet Forwarding FIB Packet Data Packet Forwarding FIB Data 5 Cognitive augmented routing system and its standardisation path March 2010

6 Learning System Knowledge Information Base (KIB): stores prior knowledge learned rules KIB Exp. E Training data set [x ; y] Learner Learning algorithm H space h f (x): approx of f y = f(x) y = h f (x) Hypothesis h f H rule (informative or predictive) + error (= training error) h f Test data set [x' ; -] Performer Learned hyp (h f ) [x' ; y' = h f (x )] Test h f Improve hf h f f target function ( learned model) [x' ; y'] 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 6 Cognitive augmented routing system and its standardisation path March 2010

7 Example: SRG Inference Infer Shared Risk Group (SRG) from LSA arrival pattern to prevent successive SPT recomputation upon shared-risk failure (affects multiple links simultaneously) Current Link-state routing Link-state Routing Machine Learning 1 5 SRG SRG 4 8 link [1,2] and [7,8] share the same risk (belong to the same SRG) 1 5 SRG SRG 4 8 Failure Failure LSA(1,2) LSA(7,8) Failure LSA(1,2) LSA(7,8) Failure First SPT Recomputation Single SPT Recomputation taking into account both link failures LSA(1,2) LSA(7,8) LSA(7,8) LSA 5 Second SPT Recomputation receive predict time 7 Cognitive augmented routing system and its standardisation path March 2010

8 ECODE Project (Experimental COgnitive Distributed ) STREP Project part of Future Internet Research and Experimentation (FIRE) Cross competences / skills and Consortium "The overall objective of the ECODE project is to investigate a new architectural component to sustain growth of an Internet that performs in accordance to what it is expected to deliver to end-users. With this 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 users with increasing heterogeneity in applicative communication needs. Networking Experimentation Machine Learning This experimentally driven research project will determine whether composing the Internet high-level goal - societal, economical, etc. - can be translated into lowerlevel objectives (in terms of functionality and performance) and constraints (both technical and non-technical) and enforced via this novel cognitive component." 8 Cognitive augmented routing system and its standardisation path March 2010

9 Cognitive augmented routing system - standardisation approach ETSI Future Network Technologies Workshop

10 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 VS 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 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 10 Cognitive augmented routing system and its standardisation path March 2010

11 Set the standardisation approach for research projects Needs a well-defined methodology 1 What do we need to standardise to allow the technology proposed by the project to be interoperable / deployable at a large scale 2 Role and impacts of standardisation bodies on the segment targeted by the research project Standardisation activities is a food chain model 3 Do we need to improve the standardisation eco-system to maximise the chance of success 4 Create new Technical Committee, working groups and/or Attract major actors Identify the structuring aspects when choosing standardisation bodies Derive a coherent standardisation approach 11 Cognitive augmented routing system and its standardisation path March 2010

12 Systematic approach for standards Cognitive augmented routing as a use case 1 What do we need to standardise? 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 Router with Machine Learning Learned rules and decisions Routing info Packet Machine Learning KIB data decisions Routing RIB Forwarding FIB Control Data 2 Role/impacts of standardisation 3 Do we need to improve the bodies and forum standardisation eco-system? None Marketing IRTF, ITU-T, ETSI IRTF, ITU-T, ETSI IRTF IETF (IRTF) Needs Architecture Solutions Test/Interop 12 Cognitive augmented routing system and its standardisation path March 2010

13 Structuring aspects of the research approach and target to standardisation 4 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 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) Protocols Advanced architecture De-risk Validate 13 Cognitive augmented routing system and its standardisation path March 2010

14 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 s 6) Formulate recommendations 7) Start base protocol engineering work 7 ) Start advanced architecture work Socialise De-risk Validate Specify First, iterate in IRTF IRTF IRTF ETSI IRTF IETF (IRTF) Specify next 14 Cognitive augmented routing system and its standardisation path March 2010

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