Autonomic computing systems adapt themselves thousands of times a second, to accomplish their goal despite changing environmental conditions and demands. The literature reports many decision mechanisms, but in most realizations a single one is applied. This paper compares of some state-of-the-art decision making approaches, applied to a self-optimizing autonomic system that allocates resources to a software application providing performance feedback at runtime, via the Application Heartbeat framework. The investigated decision mechanisms range from heuristics to control theory and machine learning: results are compared by means of case studies using standard benchmarks.

Decision Making in Autonomic Computing Systems: Comparison of Approaches and Techniques

MAGGIO, MARTINA;SANTAMBROGIO, MARCO DOMENICO;LEVA, ALBERTO
2011-01-01

Abstract

Autonomic computing systems adapt themselves thousands of times a second, to accomplish their goal despite changing environmental conditions and demands. The literature reports many decision mechanisms, but in most realizations a single one is applied. This paper compares of some state-of-the-art decision making approaches, applied to a self-optimizing autonomic system that allocates resources to a software application providing performance feedback at runtime, via the Application Heartbeat framework. The investigated decision mechanisms range from heuristics to control theory and machine learning: results are compared by means of case studies using standard benchmarks.
2011
Proc. 8th ACM International Conference on Autonomic computing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/663975
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