research-article

Dynamic resource allocation for an energy efficient VM architecture for cloud computing

Authors:

Deafallah Alsadie,

Eidah J. Alzahrani,

Albert Y. ZomayaAuthors Info & Claims

ACSW '18: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 16, Pages 1 - 8

https://doi.org/10.1145/3167918.3167952

Published: 29 January 2018 Publication History

Abstract

Minimizing power consumption is a vital consideration in the modern-day development of cloud computing. One of the major challenges reported in cloud computing is the consumption of power by computing resources due to improper allocation of resources over improperly sized virtual machines (VM). In spite of many efforts, the existing solutions are only able to meet the requirement for minimizing power consumption to a limited extent, due to their lack of optimized allocation of computing resources. The primary aim of the proposed work is to make effective use of the computing resources of the cloud for minimizing power consumption. It employs the concept of mapping appropriately sized VMs to a group of tasks in a data center, in order to reduce its power consumption. It involves the clustering of tasks on the basis of their computing requirements and finding a suitably sized VM with the required computing resources. The efficient use of computing resources on the basis of their actual requirements for a group of tasks helps to save a substantial amount of power. The proposed work is evaluated for its superiority over representational techniques using Google cloud traces as benchmark dataset. The experimental results showed an improvement of 8.42% in power consumption compared to representational techniques using fixed-sized VMs in the field. The proposed approach also achieves an improvement of 62% in the number of instances of VMs created for hosting the task workload, while maintaining a low task rejection rate.

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Cited By

Singh KVerma AKumar ABhartee AMishra A(2024)A Survey on Efficient Utilization of Virtual Machines in Cloud Computing2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)10.1109/ICETSIS61505.2024.10459622(1459-1467)Online publication date: 28-Jan-2024
https://doi.org/10.1109/ICETSIS61505.2024.10459622
Patra MSahoo BTuruk AMisra S(2023)Task grouping and optimized deep learning based VM sizing for hosting containers as a serviceJournal of Cloud Computing10.1186/s13677-023-00441-712:1Online publication date: 25-Apr-2023
https://doi.org/10.1186/s13677-023-00441-7
Qi SMilojicic DBash CPasricha S(2023)MOSAIC: A Multi-Objective Optimization Framework for Sustainable Datacenter Management2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)10.1109/HiPC58850.2023.00046(51-60)Online publication date: 18-Dec-2023
https://doi.org/10.1109/HiPC58850.2023.00046
Show More Cited By

Index Terms

Dynamic resource allocation for an energy efficient VM architecture for cloud computing
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing

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cover image ACM Other conferences

ACSW '18: Proceedings of the Australasian Computer Science Week Multiconference

January 2018

404 pages

ISBN:9781450354363

DOI:10.1145/3167918

Conference Chair:
David Abramson

Copyright © 2018 ACM.

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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New York, NY, United States

Publication History

Published: 29 January 2018

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ACSW 2018

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CORE

ACSW 2018: Australasian Computer Science Week 2018

January 29 - February 2, 2018

Queensland, Brisband, Australia

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ACSW '18 Paper Acceptance Rate 49 of 96 submissions, 51%;

Overall Acceptance Rate 204 of 424 submissions, 48%

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Cited By

Singh KVerma AKumar ABhartee AMishra A(2024)A Survey on Efficient Utilization of Virtual Machines in Cloud Computing2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)10.1109/ICETSIS61505.2024.10459622(1459-1467)Online publication date: 28-Jan-2024
https://doi.org/10.1109/ICETSIS61505.2024.10459622
Patra MSahoo BTuruk AMisra S(2023)Task grouping and optimized deep learning based VM sizing for hosting containers as a serviceJournal of Cloud Computing10.1186/s13677-023-00441-712:1Online publication date: 25-Apr-2023
https://doi.org/10.1186/s13677-023-00441-7
Qi SMilojicic DBash CPasricha S(2023)MOSAIC: A Multi-Objective Optimization Framework for Sustainable Datacenter Management2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)10.1109/HiPC58850.2023.00046(51-60)Online publication date: 18-Dec-2023
https://doi.org/10.1109/HiPC58850.2023.00046
Mishra PMoustafa A(2023)Reinforcement learning based monotonic policy for online resource allocationFuture Generation Computer Systems10.1016/j.future.2021.09.023138(313-327)Online publication date: Jan-2023
https://doi.org/10.1016/j.future.2021.09.023
Saidi KHioual OSiam A(2021)Novel energy-aware approach to resource allocation in cloud computingMultiagent and Grid Systems10.3233/MGS-21035017:3(197-218)Online publication date: 20-Dec-2021
https://doi.org/10.3233/MGS-210350
Aloufi ODjemame KSaeed FGhaban F(2021)A survey on predicting workloads and optimizing QoS in the cloud computing2021 International Congress of Advanced Technology and Engineering (ICOTEN)10.1109/ICOTEN52080.2021.9493436(1-7)Online publication date: 4-Jul-2021
https://doi.org/10.1109/ICOTEN52080.2021.9493436
Alsadie D(2021)TSMGWO: Optimizing Task Schedule Using Multi-Objectives Grey Wolf Optimizer for Cloud Data CentersIEEE Access10.1109/ACCESS.2021.30637239(37707-37725)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3063723
Ramasamy VThalavai Pillai S(2020)An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environmentCluster Computing10.1007/s10586-020-03118-xOnline publication date: 5-May-2020
https://doi.org/10.1007/s10586-020-03118-x
Dewangan BAgarwal AChoudhury TPasricha AChandra Satapathy S(2020)Extensive review of cloud resource management techniques in industry 4.0: Issue and challengesSoftware: Practice and Experience10.1002/spe.281051:12(2373-2392)Online publication date: 21-Feb-2020
https://doi.org/10.1002/spe.2810
Saidi KHioual OSiam A(2019)A DSL-MCDA Model for Energy Consumption-Aware in Cloud Computing2019 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)10.1109/IINTEC48298.2019.9112111(168-173)Online publication date: Dec-2019
https://doi.org/10.1109/IINTEC48298.2019.9112111
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