Titel
Intelligent Decision-Making in Distributed Dynamic VM Consolidation Using Fuzzy Q-Learning
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Abstract
A cloud manager deals with a dynamic multi objective opti- mization problem. Indeed, this problem lies in the fact that there is always a tradeoff between energy and performance in a virtualized data center. Therefore, a cloud manager must be equipped with a strategy to consolidate virtual ma- chines and configure them dynamically in a way that opti- mizes energy-performance tradeoff in an online manner. Dis- tributed dynamic VM consolidation strategy can be an effec- tive one to tackle this problem. The procedure of this strat- egy can be decomposed into four decision-making tasks.1) Host overloading detection; 2) VM selection; 3) Host un- derloading detection; and 4) VM placement. The dynamic optimization is achieved when each of aforementioned de- cisions are made optimally in an online manner. In this paper with concentration on host overloading detection and VM selection task, we propose the Fuzzy Q-Learning (FQL) as an intelligent and online machine learning approach in order to make optimal decisions towards dynamic energy- performance tradeoff optimization.
Stichwort
AlgorithmsDynamic VM ConsolidationFuzzy Q-LearningEnergy Efficient Cloud ManagerArtificial Intelligence
Sprache
Englisch [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:300625
Erschienen in
Titel
Karin Anna Hummel (ed.), 31st International Symposium on Computer Performance, Modeling, Measurements and Evaluation 2013: Student Poster Abstracts
Verlag
Forschungsgruppe Entertainment Computing, Fakultät für Informatik, University of Vienna
Erscheinungsdatum
24.09.2013
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