1 / 11

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

cfelker
Download Presentation

Autonomic Computing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related