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Autonomic Computing

Autonomic Computing. A Knowledge Plane for the Internet, D. Clark, J. Ramming, J. Wroclawski, SIGCOMM , August. 2003. 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

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Autonomic Computing

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  1. Autonomic Computing A Knowledge Plane for the Internet, D. Clark, J. Ramming, J. Wroclawski, SIGCOMM, August. 2003. . 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 SystemsEECS,Northwestern University

  3. 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 SystemsEECS,Northwestern University

  4. CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  5. 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 SystemsEECS,Northwestern University

  6. 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 SystemsEECS,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 SystemsEECS,Northwestern University

  8. 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 CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  9. CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  10. CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  11. 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 SystemsEECS,Northwestern University

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