1. Autonomic Computing
A Knowledge Plane for the Internet, D. Clark, J. Ramming, J. Wroclawski, SIGCOMM, August .
David Choffnes, Winter 2006

2. 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
CS 395/495 Autonomic Computing Systems EECS, Northwestern University

3. CS 395/495 Autonomic Computing Systems EECS, Northwestern University
A Knowledge Plane
Distributed cognitive system
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
CS 395/495 Autonomic Computing Systems EECS, Northwestern University

4. CS 395/495 Autonomic Computing Systems EECS, Northwestern University

5. CS 395/495 Autonomic Computing Systems EECS, Northwestern University
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
CS 395/495 Autonomic Computing Systems EECS, Northwestern University

6. CS 395/495 Autonomic Computing Systems EECS, Northwestern University
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
CS 395/495 Autonomic Computing Systems EECS, Northwestern University

7. 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
CS 395/495 Autonomic Computing Systems EECS, Northwestern University

8. Functional/Structural Requirements
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
CS 395/495 Autonomic Computing Systems EECS, Northwestern University

9. CS 395/495 Autonomic Computing Systems EECS, Northwestern University

10. CS 395/495 Autonomic Computing Systems EECS, Northwestern University

11. CS 395/495 Autonomic Computing Systems EECS, Northwestern University
Creating a KP
Building blocks
Epidemic algs (dist), Bayesian NWs (learning), rank aggregation (trust), constraint satisfaction algs, policy-based management.
Challenges
Representing and utilizing knowledge
Scalability
Routing knowledge
Economic incentives
Malicious users and trust
CS 395/495 Autonomic Computing Systems EECS, Northwestern University