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Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems

Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems MARTINA MAGGIO, Lund University, Sweden HENRY HOFFMANN, Massachusetts Institute of Technology, Cambridge, MA ALESSANDRO V. PAPADOPOULOS, JACOPO PANERATI, and MARCO D. SANTAMBROGIO, Politecnico di Milano, Italy ANANT AGARWAL, Massachusetts Institute of Technology, Cambridge, MA ALBERTO LEVA, Politecnico di Milano, Italy 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

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References (41)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/2382570.2382572
Publisher site
See Article on Publisher Site

Abstract

Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems MARTINA MAGGIO, Lund University, Sweden HENRY HOFFMANN, Massachusetts Institute of Technology, Cambridge, MA ALESSANDRO V. PAPADOPOULOS, JACOPO PANERATI, and MARCO D. SANTAMBROGIO, Politecnico di Milano, Italy ANANT AGARWAL, Massachusetts Institute of Technology, Cambridge, MA ALBERTO LEVA, Politecnico di Milano, Italy 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

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: Dec 1, 2012

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