Open Access

ARTICLE

Efficient Energy-Aware Resource Management Model (EEARMM) Based Dynamic VM Migration

by V. Roopa^1,*, K. Malarvizhi², S. Karthik³

1 Department of Information Technology, Sri Krishna College of Technology, Coimbatore, 641042, India
2 Department of Electrical & Electronics Engineering, Kumaraguru College of Technology, Coimbatore, 641049, India
3 Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, 641035, Tamilnadu, India

* Corresponding Author: V. Roopa. Email:

Computer Systems Science and Engineering 2022, 43(2), 657-669. https://doi.org/10.32604/csse.2022.022173

Received 29 July 2021; Accepted 22 October 2021; Issue published 20 April 2022

Abstract

In cloud environment, an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands. The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure. However, it is very difficult to persuade the objectives of the Cloud Service Providers (CSPs) and end users in an impulsive cloud domain with random changes of workloads, huge resource availability and complicated service policies to handle them, With that note, this paper attempts to present an Efficient Energy-Aware Resource Management Model (EEARMM) that works in a decentralized manner. Moreover, the model involves in reducing the number of migrations by definite workload management for efficient resource utilization. That is, it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine. The Estimation Model Algorithm (EMA) is given for determining the virtual machine migration. Further, VM-Selection Algorithm (SA) is also provided for choosing the appropriate VM to migrate for resource management. By the incorporation of these algorithms, overloading of VM instances can be avoided and energy efficiency can be improved considerably. The performance evaluation and comparative analysis, based on the dynamic workloads in different factors provides evidence to the efficiency, feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.

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Keywords

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