An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Devesh Kumar Srivastava; Pradeep Kumar Tiwari; Mayank Srivastava; Babu R Dawadi

doi:10.1155/2022/5324202

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Comput Intell Neurosci. 2022 Aug 25:2022:5324202. doi: 10.1155/2022/5324202. eCollection 2022.

Authors

Devesh Kumar Srivastava¹, Pradeep Kumar Tiwari¹, Mayank Srivastava², Babu R Dawadi³

Affiliations

¹ Manipal University Jaipur, Jaipur, India.
² Department of ECE, National Institute of Technology, Jamshedpur, Jharkhand, India.
³ Department of Electronics and Computer Engineering, Faculty of Computer Engineering, Institute of Engineering-Pulchowk Campus, Tribhuvan University, Kirtipur, Nepal.

Abstract

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.