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