Applying genetic algorithms to decision making in autonomic computing systems

  • Authors:

  • Andres J. Ramirez;David B. Knoester;Betty H.C. Cheng;Philip K. McKinley

  • Affiliations:

  • Michigan State University, East Lansing, MI, USA;Michigan State University, East Lansing, MI, USA;Michigan State University, East Lansing, MI, USA;Michigan State University, East Lansing, MI, USA

  • Venue:
  • ICAC '09 Proceedings of the 6th international conference on Autonomic computing
  • Year:
  • 2009

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Abstract

Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means for automating software maintenance tasks. As the complexity of adaptive and autonomic systems grows, designing and managing the set of reconfiguration rules becomes increasingly challenging and may produce inconsistencies. This paper proposes an approach to leverage genetic algorithms in the decision-making process of an autonomic system. This approach enables a system to dynamically evolve reconfiguration plans at run time in response to changing requirements and environmental conditions. A key feature of this approach is incorporating system and environmental monitoring information into the genetic algorithm such that specific changes in the environment automatically drive the evolutionary process towards new viable solutions. We have applied this genetic-algorithm based approach to the dynamic reconfiguration of a collection of remote data mirrors, with the goal of minimizing costs while maximizing data reliability and network performance, even in the presence of link failures.