Comparison of Decision Making Strategies for Self-Optimization in Autonomic Computing Systems
Author
Summary, in English
Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This paper proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application. A variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks. Our results indicate that the most suitable decision mechanism can vary depending on the specific test case but adaptive and model predictive control systems tend to produce good performance and may work best in a priori unknown situations.
Department/s
Publishing year
2012
Language
English
Publication/Series
ACM Transactions on Autonomous and Adaptive Systems
Volume
7
Issue
4
Document type
Journal article
Publisher
Association for Computing Machinery (ACM)
Topic
- Control Engineering
Keywords
- Algorithms
- Design
- Performance
- Decision mechanisms
- comparison
- design approaches
Status
Published
Research group
- LCCC
ISBN/ISSN/Other
- ISSN: 1556-4665