Knowledge Plane and
Context-based management
Kaisa Kettunen
Helsinki University of Technology / S-38.4030
Seminar 26.-29.5.2006
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
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generality and heterogeneity
rich end-system functionality
decentralized, multi-administrative structure
but it is also responsible for the existing limitations
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frustrated users when something fails
high management overhead (manual configuration, diagnosis, design)
Kaisa Kettunen
Helsinki University of Technology/S-38.4030
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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
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Security
Quality of Service
Roaming (e.g. billing and authentication)
Kaisa Kettunen
Helsinki University of Technology/S-38.4030
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Context Aware Applications
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Adapt behavior with minimum user attention based on available sensor information,
which has been converted into the format and level needed by the application
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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
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CE and CAA are abstractions of a data source or processing component, which actively query
events from (other) CE entities
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Helsinki University of Technology/S-38.4030
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Knowledge Plane (KP)
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Pervasive system within the network
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Enhances ability to manage the network intelligently without disturbing the
control and data planes
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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
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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
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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
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Knowledge Plane Architecture
Knowledge
Plane
assertions
Knowledge
(cognitive computations)
Sensor
observations
explanations
Actuator
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
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What is Knowledge Plane good for?
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Fault diagnosis and mitigation
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Automatic (re)configuration
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Continous and recursive detection and adjustment of configuration to be the
optimal
Support for overlay networks
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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
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Data collection and gathering basis for next generation tools with several
observation points
Kaisa Kettunen
Helsinki University of Technology/S-38.4030
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Sophia – Knowledge Plane incarnation
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Distributed system deployed on PlanetLab that stores, propagates, aggregates
and reacts to observations on network conditions without the learning aspect of
Knowledge Plane.
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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)
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Example: eval(bandwidth(env(node(id42),
time(Sometime)),
BwVar))
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Each node’s local core implemented as loadable modules with
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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
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Semantic-Enhanced Distribution & Adaptation Networks (SEDAN)
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Content delivery and adaptation managed by maintained sematic information on
content, infrastructure and clients
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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
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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)
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Protection routing algorithms on optical (GMPLS over WDM) networks
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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
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Chaotic (unplanned and unmanaged) wireless networks may be improved in several
aspects with help of Knowledge Plane
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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
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Context-based management provides means for improving the
currently complex network configuration and control
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Knowledge Plane introduces a new cognitive information layer aside
the control and data planes for intelligent network management
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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 QoS
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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
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References
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A Knowledge Plane for the Internet, David D. Clark, Craig Partridge, J. Christopher
Ramming and John T. Wroclawski, SIGCOMM, 2003
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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
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A Knowledge Plane as a Pricing Mechanism for Aggregate, User-Centric Utility
Maximization, Vladimir Marbukh
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Semantic-Enhanced Distribution & Adaptation Networks, Bo Shen, Zhichen Xu, Susie
Wee and John Apostolopoulos, IEEE International Conference on Multimedia and Expo (ICME),
2004
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Adding new Components to the Knowledge Plane in GMPLS over WDM Networks,
Anna Urra, Eusebi Calle, J.L. Marzo, IEEE, 2004
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Self-Management in Chaotic Wireless Deployments, Aditya Akella, Glenn Judd, Srinivasan
Seshan and Peter Steenkiste, MobiCom 2005
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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
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