1 / 8

Autonomic Computing

Autonomic Computing. The vision of autonomic computing, J. Kephart and D. Chess, IEEE Computer , Jan. 2003. Also - A.G. Ganek and T.A. Corbi, “The dawning of the autonomic computing era”, IBM Systems Journal, 42 (1), 2003.

loring
Télécharger la présentation

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 The vision of autonomic computing, J. Kephart and D. Chess, IEEE Computer, Jan. 2003. Also - A.G. Ganek and T.A. Corbi, “The dawning of the autonomic computing era”, IBM Systems Journal, 42 (1), 2003. - R. Want, T. Pering and D. Tennehouse, “Comparing autonomic and proactive computing”, IBMS Systems Journal, 42 (1), 2003. . Fabián E. Bustamante, Winter 2006

  2. The problem • The main obstacle to further progress in IT industry • Not a change in Moore’s law, but • Looming software complexity crisis • Beyond admin single environments, to integration into intra- and inter-corporate computing systems “Complexity is the business we are in, and complexity is what limits us.”, Fred Brooks Jr. • Better programming won’t do it • Consider • ~1/3 to ½ of a company’s total IT budget goes to preventing and recovering from crashes • “For every dollar to purchase storage, you spend $9 to have someone manage it.”, N. Tabellion, CTO Fujitsu Softek • ~40% of computer outages are caused by operator errors • Average downtime impact for IT ~ $1.4 millions revenue/hour CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  3. The answer/hope – Autonomic computing • Autonomic systems – can manage themselves given high-level objectives from admins. ~ autonomic nervous system • An autonomic system • Knows itself • Knows its environment & the context surrounding its activity • (Re) configure itself under varying and unpredictable conditions • Is always on the look to optimize its working • Is able to protect and heal itself • Anticipates the optimized resources needed to meet a user’s information needs • To incorporate these characteristics, it must have the following properties/features … CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  4. Self-* properties • Self-configuration • Current: Data centers made of components from/for multiple vendors/platforms; installation, configuration & integration is time consuming & error prone • Autonomic: Automated based high-level policies, host system adjust itself automatically and seamalessly • Self-optimization • Current: Hundreds of manually set, nonlinear tuning knobs • Autonomic: Components and system continually seek optimization opportunities • Self-healing • Current: e.g. problem determination can take weeks • Autonomic: self detection, diagnosis, and repair for HW&SW • Self-protection • Current: Detection & recovery from attacks & cascading failures is manual • Autonomic: Self-defense using early warning to anticipate & prevent system-wide failures CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  5. Autonomic manager Analyze Plan Knowledge Monitor Execute Managed element Autonomic element • Autonomic systems – interactive collection of autonomic elements • Autonomic element • 1+ managed elements + autonomic manager that controls it • Function at many levels – from disk drives to entire enterprises • Fixed behavior, connections and relationships gives away to increased dynamism and flexibility expresed as high-level goals CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  6. Evolution to autonomic systems CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  7. Engineering challenges • Design, test and verification • Installation and configuration • Monitoring, problem determination, upgrading • Managing the life cycle • Autonomic systems will have multiple elements at different stages, handling multiple tasks, … how to handle all? • Relationships among autonomic elements • Specification of services needed/provided; ways to locate providers; ways to establish SLA; … • Robustness against self-management-based attacks • Goal specification and robustness to wrongly specified goals CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

  8. Scientific challenges • How to understand, control, and design emergent behavior • Understanding the mapping from local to global behavior is not enough • Develop a theory of robustness • Beginning with a definition • Learning and optimization theory • Machine learning by a single element in static environment is just the basic – multiagent systems in dynamic environments • Negotiation theory • How should the multiple elements negotiate? • Automated statistical modeling • Statistical modeling for detection/prediction of performance models; ways to aggregate statistical variables to reduce dimensionality CS 395/495 Autonomic Computing SystemsEECS,Northwestern University

More Related