Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems
Abstract
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 article 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2012
- Source ID
- 10.1145/2382570.2382572
Entities
People
- Alberto Leva
- Alessandro V. Papadopoulos
- Anant Agarwal
- Henry Hoffmann
- Jacopo Panerati
- Marco D. Santambrogio
- Martina Maggio
Organizations
- Defense Advanced Research Projects Agency
- Federal Government of the United States
- Lund University
- Massachusetts Institute of Technology
- Polytechnic University of Milan
- Swedish Research Council