research-article

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

Authors:

Martina Maggio,

Henry Hoffmann,

Alessandro V. Papadopoulos,

Jacopo Panerati,

Marco D. Santambrogio,

Alberto LevaAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 7, Issue 4

Article No.: 36, Pages 1 - 32

https://doi.org/10.1145/2382570.2382572

Published: 01 December 2012 Publication History

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.

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

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 7, Issue 4

Special Section: Extended Version of SASO 2011 Best Paper

December 2012

167 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2382570

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 December 2012

Accepted: 01 April 2012

Revised: 01 January 2012

Received: 01 October 2011

Published in TAAS Volume 7, Issue 4

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Cited By

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https://dl.acm.org/doi/10.1145/3563835.3567655
Zaker FLitoiu MShtern M(2022)Formally Verified Scalable Look Ahead Planning For Cloud Resource ManagementACM Transactions on Autonomous and Adaptive Systems10.1145/355531517:3-4(1-23)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.1145/3555315
Wang SHoffmann HLu SRoychoudhury ACadar CKim M(2022)AgileCtrl: a self-adaptive framework for configuration tuningProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3549136(459-471)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3540250.3549136
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Caldas RRodrigues AGil ERodrigues GVogel TPelliccione PHoniden SNitto ECalinescu R(2020)A hybrid approach combining control theory and AI for engineering self-adaptive systemsProceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/3387939.3391595(9-19)Online publication date: 29-Jun-2020
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