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Optimizing Energy Consumption for Cloud Computing: A Cluster and Migration Based Approach (CMBA)

Published: 04 March 2020 Publication History

Abstract

The increased use of IT technologies and number of IT users have triggered cloud computing resource demand including the need for more data centers. Each data center consumes electricity for its un-interrupted operations and maintenance, therefore responsible for the emissions of carbon dioxide, a potent greenhouse gas causing climate change. Hence, there is a necessity to provide a solution through which energy consumption for cloud data centers can be reduced. As virtual machine located in data center are run under loaded to maintain higher performance but it causes wastage of resources and power. While, task overloading severally reduce the performance of data center. To address this issue, we propose CMBA (Cluster and Migration Based Approach) for cloud resource allocation that maps groups of tasks to customized virtual machine types based on processing, memory and network requirements. Proper placement of workload with specific VMs and dynamic migration concept reduce energy consumption for running physical machine and its respective host or data centers. Taking altogether, intelligent customization of virtual machines by adopting CMBA approach will maintain high efficiency of datacenters with reduced energy consumption.

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  • (2022)Energy-Efficient Resource Management of Virtual Machine in Cloud InfrastructureNew Frontiers in Cloud Computing and Internet of Things10.1007/978-3-031-05528-7_4(107-131)Online publication date: 27-Sep-2022

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    CSAI '19: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence
    December 2019
    370 pages
    ISBN:9781450376273
    DOI:10.1145/3374587
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Shenzhen University: Shenzhen University

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 04 March 2020

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    Author Tags

    1. Cloud computing
    2. energy consumption
    3. virtual machine migration
    4. virtualization

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    • (2022)Energy-Efficient Resource Management of Virtual Machine in Cloud InfrastructureNew Frontiers in Cloud Computing and Internet of Things10.1007/978-3-031-05528-7_4(107-131)Online publication date: 27-Sep-2022

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