Knowledge Plane and Context-based management Kaisa Kettunen Helsinki University of Technology / S-38. 4030 Seminar 26. -29. 5. 2006 Kaisa Kettunen Helsinki University of Technology/S-38. 4030
Internet today Internet has become a global communication medium. The success derives from the fundamental design principle ”simple and transparent core with intelligence at the edges” which is behind the strength of the Internet + + + generality and heterogeneity rich end-system functionality decentralized, multi-administrative structure but it is also responsible for the existing limitations - frustrated users when something fails high management overhead (manual configuration, diagnosis, design) Kaisa Kettunen Helsinki University of Technology/S-38. 4030 2
Context-based management Ambition towards dynamic operating environment for improved and more automated management Contextual approach: Collective actions to support and provide a desired global outcome This suggests a pervasive and context aware environment, which would allow network administrators to view the status and performance of their devices on a variety of statistics and thus improve planning and management of the network in terms of for example q q q Security Quality of Service Roaming (e. g. billing and authentication) Kaisa Kettunen Helsinki University of Technology/S-38. 4030 3
Context Aware Applications n Adapt behavior with minimum user attention based on available sensor information, which has been converted into the format and level needed by the application q q n Emphasis on using information instead of obtaining it Decomposition of the application into entities providing building blocks Loose coupling between applications and needed data Specification of data by its properties rather than physical location Context Servers (CS) provide maintenance, messaging, registration, configuration and mobility services to Context Entities (CE) and Context Aware Applications (CAA) in their range and enable interaction towards other ranges q CE and CAA are abstractions of a data source or processing component, which actively query events from (other) CE entities E E A Kaisa Kettunen A CS E A E A A CS E E A Helsinki University of Technology/S-38. 4030 E A A CS E E A 4
Knowledge Plane (KP) n Pervasive system within the network q n Enhances ability to manage the network intelligently without disturbing the control and data planes q q n Builds and maintains information on network behaviour to the users, operators and to itself Assembly from high level instructions and re-assembly on changes Automatic problem detection and fixing with indication if not possible Cognitive system q q Learn & reason to act or propose actions accordingly Ability to handle and perform with conflicting or wrong information or high-level goals Kaisa Kettunen Helsinki University of Technology/S-38. 4030 5
Attributes of the KP Edge involvement Global perspective ”Knowledge” produced, managed and consumed beyond traditional edge of the network Information from edges combined with data from different parts of network Cognitive framework Compositional structure Respond, reason, mediate and automate to be aware Operate in presence of imperfect information and different objectives Unified approach Common standards and framework to structure based on knowledge, not the task Kaisa Kettunen Helsinki University of Technology/S-38. 4030 6
Knowledge Plane Architecture Knowledge (cognitive computations) Sensor observations explanations Actuator Knowledge Plane assertions Internet Information handling and control Observations describe current conditions Assertions capture high-level goals, intentions and constraints on network operations Explanations create conclusions from observations and assertions Learning and environment altering Sensors are entities that produce observations Actuators are entities that change behavior (e. g. change routing tables or bring links up or down) Knowledge is based on cognitive computation realized by artificial intelligence (AI) algorithms Kaisa Kettunen Helsinki University of Technology/S-38. 4030 7
What is Knowledge Plane good for? n Fault diagnosis and mitigation q n Automatic (re)configuration q n Continous and recursive detection and adjustment of configuration to be the optimal Support for overlay networks q n Learning combined diagnosis and mitigation with interaction towards the user Instead of application level probing to evaluate and seek better paths, use application and network information collected and offered Knowledge-enhanced intrusion detection q Data collection and gathering basis for next generation tools with several observation points Kaisa Kettunen Helsinki University of Technology/S-38. 4030 8
Sophia – Knowledge Plane incarnation n Distributed system deployed on Planet. Lab that stores, propagates, aggregates and reacts to observations on network conditions without the learning aspect of Knowledge Plane. q n System optimizing its performance on caching, evaluation scheduling and planning Computational model using declarative programming language based on Prolog for evaluating and expressing application domain statements through logic rules, facts and expressions (instruction set) q Example: eval(bandwidth(env(node(id 42), time(Sometime)), Bw. Var)) n Each node’s local core implemented as loadable modules with q q q Logic terms database which can be updated to extend the system Local unification engine based on standard logic unification I/O interfaces towards sensors and actuators Remote evaluator handling networking and protocol towards other nodes for delegating tasks Expression scheduling mechanism for maintaining calendar for future scheduled evaluations Kaisa Kettunen Helsinki University of Technology/S-38. 4030 9
Examples n Semantic-Enhanced Distribution & Adaptation Networks (SEDAN) q Content delivery and adaptation managed by maintained sematic information on content, infrastructure and clients n q q q n E. g. Semantic-accurate content adaptation under resource constraints Formally defined data model used to organize and store information, e. g. scenes of a movie (content), service processing requirements (services), locations of network resources (resources) or user profiles (clients) Knowledge plane used for semantic information sharing between components Distributed decision making on decisions plane utilizing knowledge plane information Pricing mechanism for aggregate, user-centric utility maximization q Manipulation of elastic users with pricing signals to gain optimal network resource usage (e. g. bandwidth or routing) Kaisa Kettunen Helsinki University of Technology/S-38. 4030 10
Examples (2) n Protection routing algorithms on optical (GMPLS over WDM) networks q q n Enhance network reliability, e. g. link failure probabilities, and thus total bandwidth consumption as well as decrease packet loss Abnormalties in link behaviour are detected based on learned link patterns and the information used to select right links or backup paths with faster routing algorithm computation Self-Management in Chaotic Wireless Deployments q Chaotic (unplanned and unmanaged) wireless networks may be improved in several aspects with help of Knowledge Plane n n q Minimize degradation on links and interference from neighbouring APs with automated power control and rate adaptation algorithms Load management and effective coverage over several APs Rate adaptation mechanisms Traffic scheduling mechnisms to optimize battery power Trace-driven simulations and small testbed used as analysis basis Kaisa Kettunen Helsinki University of Technology/S-38. 4030 11
Conclusions n Context-based management provides means for improving the currently complex network configuration and control n Knowledge Plane introduces a new cognitive information layer aside the control and data planes for intelligent network management n The principle of Knowledge Plane can be adapted and used in several areas and environments aside Internet to ensure a common goal, e. g. end-2 -end Qo. S n Together with intelligent and elastic user applications, a selfmanaged and self-organized pervasive system can be established Kaisa Kettunen Helsinki University of Technology/S-38. 4030 12
References n A Knowledge Plane for the Internet, David D. Clark, Craig Partridge, J. Christopher Ramming and John T. Wroclawski, SIGCOMM, 2003 n Sophia: An Information Plane for Networked Systems, Mike Wawrzoniak, Larry Peterson and Timothy Roscoe, ACM SIGCOMM Computer Communications Review, Vol 34, Nr 1, Jan 2004 n A Knowledge Plane as a Pricing Mechanism for Aggregate, User-Centric Utility Maximization, Vladimir Marbukh n Semantic-Enhanced Distribution & Adaptation Networks, Bo Shen, Zhichen Xu, Susie Wee and John Apostolopoulos, IEEE International Conference on Multimedia and Expo (ICME), 2004 n Adding new Components to the Knowledge Plane in GMPLS over WDM Networks, Anna Urra, Eusebi Calle, J. L. Marzo, IEEE, 2004 n Self-Management in Chaotic Wireless Deployments, Aditya Akella, Glenn Judd, Srinivasan Seshan and Peter Steenkiste, Mobi. Com 2005 n Towards a Reliable, Wide-Area Infrastructure for Context-Based Self-Management of Communications, Graeme Stevenson, Paddy Nixon and Simon Dobson, UCD Systems Research Group, Dublin, 2005 Kaisa Kettunen Helsinki University of Technology/S-38. 4030 13
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