Energy-efficient resource hyper-visioning in private clouds

The term “private Cloud” refers to a group of computers working together within a business. In this thesis the energy consumption in private Clouds is reduced. This reduction is achieved through adaptive resource hyper-visioning decisions. Its adaptiveness comes from its ability to learn from the relation between resource hyper-visioning decisions and energy consumption level. The proposed adaptive resource hyper-visioning mechanisms use a statistical technique called Bayesian Inference. Bayesian Inference based mechanisms reduced energy consumption and shortened execution time. Such energy reduction translates into the elimination of tons of Carbon dioxide emission annually.