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A Comparison of Autonomic Decision Making Techniques

Author(s)
Maggio, Martina; Hoffmann, Henry; Santambrogio, Marco D.; Agarwal, Anant; Leva, Alberto
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DownloadMIT-CSAIL-TR-2011-019.pdf (430.0Kb)
Other Contributors
Computer Architecture
Advisor
Anant Agarwal
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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, which provides direct performance feedback at runtime. The Application Heartbeats framework is used to provide the sensor data (feedback), and 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.
Date issued
2011-04-01
URI
http://hdl.handle.net/1721.1/62020
Series/Report no.
MIT-CSAIL-TR-2011-019

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