General-Purpose Autonomic Computing

  • Radu Calinescu


The success of mainstream computing is largely due to the widespread availability of general-purpose architectures and of generic approaches that can be used to solve real-world problems cost-effectively and across a broad range of application domains. In this chapter, we propose that a similar generic framework is used to make the development of autonomic solutions cost effective, and to establish autonomic computing as a major approach to managing the complexity of today’s large-scale systems and systems of systems. To demonstrate the feasibility of general-purpose autonomic computing, we introduce a generic autonomic computing framework comprising a policy-based autonomic architecture and a novel four-step method for the effective development of self-managing systems. A prototype implementation of the reconfigurable policy engine at the core of our architecture is then used to develop autonomic solutions for case studies from several application domains. Looking into the future, we describe a methodology for the engineering of self-managing systems that extends and generalises our autonomic computing framework further.


Autonomic System Manageability Adaptor Autonomic Computing Dynamic Power Management Probabilistic Model Checker 
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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The work presented in this chapter was partly supported by the UK Engineering and Physical Sciences Research Council grant EP/F001096/1. The author is grateful to Marta Kwiatkowska, David Parker, Gethin Norman and Mark Kattenbelt for insightful discussions during the integration of the PRISM probabilistic model checker with the autonomic policy engine.


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Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Computing LaboratoryUniversity of OxfordOxfordEngland, UK

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