An Energy-Efficient Scheme for Cloud Resource Provisioning Based on CloudSim

Abstract

Cloud computing has recently received considerable attention. With the fast development of cloud computing, the data center is becoming larger in scale and consumes more energy. There is an emergency need to develop efficient energy-saving methods to reduce the huge energy consumption in the cloud data center. In this paper, we achieve this goal by dynamically allocating resources based on utilization analysis and prediction. We use ``Linear Predicting Method" (LPM) and ``Flat Period Reservation-Reduced Method" (FPRRM) to get useful information from the resource utilization log, and make M/M/1 queuing theory predicting method have better response time and less energy-consuming. Experimental evaluation performed on CloudSim cloud simulator shows that the proposed methods can effectively reduce the violation rate and energy-consuming in the cloud.