Comparison of Decision Making Strategies for Self-Optimization in Autonomic Computing Systems
(2012) In ACM Transactions on Autonomous and Adaptive Systems 7(4).- 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 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2518882
- author
- Maggio, Martina LU ; Hoffmann, Henry ; Papadopoulos, Alessandro Vittorio ; Panerati, Jacopo ; Santambrogio, Marco Domenico ; Agarwal, Anant and Leva, Alberto
- organization
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Algorithms, Design, Performance, Decision mechanisms, comparison, design approaches
- in
- ACM Transactions on Autonomous and Adaptive Systems
- volume
- 7
- issue
- 4
- publisher
- Association for Computing Machinery (ACM)
- external identifiers
-
- wos:000312415700002
- scopus:84870678695
- ISSN
- 1556-4665
- DOI
- 10.1145/2382570.2382572
- language
- English
- LU publication?
- yes
- id
- d6742731-ce2d-4abc-892a-be9a827e49e5 (old id 2518882)
- date added to LUP
- 2016-04-01 10:15:38
- date last changed
- 2022-03-04 17:50:37
@article{d6742731-ce2d-4abc-892a-be9a827e49e5,
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 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.}},
author = {{Maggio, Martina and Hoffmann, Henry and Papadopoulos, Alessandro Vittorio and Panerati, Jacopo and Santambrogio, Marco Domenico and Agarwal, Anant and Leva, Alberto}},
issn = {{1556-4665}},
keywords = {{Algorithms; Design; Performance; Decision mechanisms; comparison; design approaches}},
language = {{eng}},
number = {{4}},
publisher = {{Association for Computing Machinery (ACM)}},
series = {{ACM Transactions on Autonomous and Adaptive Systems}},
title = {{Comparison of Decision Making Strategies for Self-Optimization in Autonomic Computing Systems}},
url = {{http://dx.doi.org/10.1145/2382570.2382572}},
doi = {{10.1145/2382570.2382572}},
volume = {{7}},
year = {{2012}},
}