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Eos: An Approach of Using Behavior Implications for Policy-Based Self-Management

  • Sandeep Uttamchandani
  • Carolyn Talcott
  • David Pease
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2867)

Abstract

Systems are becoming exceedingly complex to manage. As such, there is an increasing trend towards developing systems that are self-managing. Policy-based infrastructures have been used to provide a limited degree of automation, by associating actions to system-events. In the context of self-managing systems, the existing policy-specification model fails to capture the following: a) The impact of a rule on system behavior (behavior implications). This is required for automated decision-making. b) Learning mechanisms for refining the invocation heuristics by monitoring the impact of rules.

This paper proposes Eos; An approach to enhance the existing policy-based model with behavior implications. The paper gives details of the following aspects:

* Expressing behavior implications.

* Using behavior implications of a rule for learning and automated decision-making.

* Enhancing existing policy-based infrastructures to support self-management using Eos.

The paper also describes an example of using Eos for self-management within a distributed file-system.

Keywords

Policy Decision Point Distribute File System Specification Template Candidate Rule Policy Enforcement Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Sandeep Uttamchandani
    • 1
  • Carolyn Talcott
    • 2
  • David Pease
    • 1
  1. 1.IBM Almaden Research CenterSan Jose
  2. 2.SRI InternationalMenlo Park

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