An Efficient Virtual Machine Placement Approach for Energy Minimizing in Cloud Environment

  • Dr . Khaldun Ibraheem Ibraheem Arif Computer Sciences Department, College of Education for pure Sciences, University of Thi-Qar
  • Rawafid Abdul Kadeem khayun Computer Sciences Department, College of Education for pure Sciences University of Thi-Qar
Keywords: Cloud Computing, VM Placement, Energy Consumption, Bin Packing


Cloud computing can be defined as an evolving Computing technology using the Internet and essential remote infrastructure to retain on-demand and pay-as-you-go assets. The number of data centers across the world has increased through wide adaptation to cloud principles, leading to large amounts of data center power consumption that affects the climate and economic aspects. So many virtual machines (VMs) could be installed on one Physical Machine(PM) via virtualization. The Cloud workload is held by these VMs and executed. Effective PM allocation of VMs will lead to better use of resources and energy savings. In this paper, our goal is to provide an improved Policy on energy-efficient VM placement to minimize energy consumption in the cloud environment, placing the VMs in the addressed bin backing mechanism and retaining the Service Level Agreement (SLA) between the provider and the cloud customer. Significant reduction in power consumption could even be made if convective are implemented at software level. Energy -aware scheduling processes produces excellent performance by implementing bin-packing energy efficiency mechanisms. An improved algorithm has been enhanced for the two First Fit Decreasing FFB and Best Fit Decreasing BFD algorithms, which are considered the best among bin packing algorithms. This algorithm adopts server power as the basis for arranging servers in the database, unlike the BFD, FFD algorithms that arrange servers according to CPU. The proposed algorithm has been practically tried using Matlab 2020 programing language for samples of servers and virtual machines, whose specifications were chosen randomly, and the results showed great efficiency of the proposed algorithm in reducing energy consumption


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