Energy Aware Scheduling of Tasks in Cloud environment

Abstract

Energy consumption in cloud data centers is increasing as the use of such services increases. It is necessary to propose new ways of reducing energy consumption. Task scheduling is an essential aspect of energy consumption in a cloud data center. The main objective of this research is to efficiently schedule various workflows so that energy consumption is minimized in the overall makespan and slice time of the processor. In this paper, the author makes a new approximation for the dynamic allocation of workflow tasks to currently available resources using genetic algorithm crossover, mutations, and evaluation operators. The purpose of this scheduling is to reduce the makespan and slack time on the processor and thus reduce the energy consumption of the processor in times that are not in use.