Transcript Autonomic Computing A Knowledge Plane for the Internet, D. Clark, J. Ramming, J.

Autonomic Computing
A Knowledge Plane for the Internet, D. Clark,
J. Ramming, J. Wroclawski, SIGCOMM,
August. 2003.
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David Choffnes, Winter 2006
The Internet is great, but…
Intelligence is only at the edges
– When failures occur, takes a long time to debug
and fix
– Difficult to configure and administer
New goal for the network
– Understand what it’s being asked to do
– Take care of itself
Internet needs AI/CogSci
– Need to abstract high-level goals from low-level
details
– Make decisions based on incomplete/imperfect
information
– Learn from previous experience/examples
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A Knowledge Plane
Distributed cognitive system
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Global vs. regional perspective
Edge involvement
Composition ability
Unified approach
Cognitive framework
• Make judgments in the face of partial/conflicting
information
• Incorporate knowledge representation, learning, reasoning
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Why?
Do we need a new construct?
– Data plane hides information, control plane exposes
everything
• Need middle ground to express goals at a high level and have
them automatically fulfilled by tuning at the low level
Unified approach
– Network measurement (everyone uses same info)
– Tracing a hurricane to the flap of a butterfly’s wings
Cognitive System
– “close the loop” on the network as does an ordinary control system
– recognize-explain cycle => recognize-explain-suggest cycle =>
recognize-act cycle for many management tasks
– the KP must be able to learn and reason
– model behavior, dependencies, and requirements of
applications
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What is it good for?
Fault diagnosis/mitigation
– WHY, FIX constructs
Automatic (re)configuration
– Ongoing operation to meet goals
– KP as assistant to network admins
Overlay networks
– KP maintains performance information
Knowledge-enhanced IDS
– Data gathering and correlation
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Knowledge Plane Architecture
Distributed organization
– Bottom-up
– Constraint-driven
• E.g., “no multicast”
• May adopt behavior not specifically constrained
– Compositional (moves from simple to complex)
Global perspective
– Data/knowledge integration
– Expect imperfect info
– Reason about tradeoffs
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Functional/Structural Requirements
Functional
– Gather/Acquire/Generate observations, assertions
and explanations about network conditions
– Cross-regional reasoning
– Knowledge-driven routing w/ understanding of
tradeoffs
– Trust/Robustness
Structural
– Sensors and actuators
– Don’t do: Each region reasons about only itself
– Maybe: Multiple regions compete to provide info
about an AS
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Creating a KP
Building blocks
– Epidemic algs (dist), Bayesian NWs (learning), rank
aggregation (trust), constraint satisfaction algs,
policy-based management.
Challenges
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Representing and utilizing knowledge
Scalability
Routing knowledge
Economic incentives
Malicious users and trust
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