An Energy & Cost Efficient Task Consolidation Algorithm for Cloud Computing Systems

Abstract

The power consumption of untapped resources, especially during a cloud background, represents a significant sum of the specific power use. By its nature, a resource allotment approach that takes into account the use of resources would direct to better power efficiency; this, in clouds, expands even additional, and with virtualization techniques often jobs are easily combined. Job consolidation is an effective way to expand the use of resources and sequentially reduce power consumption. Current studies have determined that server power utilization extends linearly with processor resources. This hopeful fact highlights the importance of the involvement of standardization to reduce energy utilization. However, merging tasks can also cause freedom from resources that will remain idle as the attraction continues. There are some remarkable efforts to decrease idle energy draw, usually by putting computer resources into some kind of power-saving/sleep mode. Throughout this article, we represent 2 power-conscious task reinforcement approaches to maximize resource use and explicitly consider both passive and active power consumption. Our inferences map each job to the resource at which the power consumption to perform the job is implicitly or explicitly reduced without degrading the performance of that task. Supporting our investigational outcome, our inference methods reveal the most promising power-saving potential.