research-article

Adaptive virtual machine migration mechanism for energy efficiency

Authors:

A. AletiAuthors Info & Claims

GREENS '16: Proceedings of the 5th International Workshop on Green and Sustainable Software

Pages 8 - 14

https://doi.org/10.1145/2896967.2896969

Published: 14 May 2016 Publication History

Abstract

Cloud systems have become a popular platform for business applications due to the flexibility in usage and payment they offer. One of the caveats of Cloud systems is their high energy consumption. Minimizing energy consumption while maintaining a high service level has become a relevant optimization task for Cloud providers. Opportunities for energy savings arise when server hosts are overloaded, which also entails unnecessary delays.

To address the problem, researchers have devised strategies how to choose the server host to deploy an application to and how to choose a running application for migration when a host has been identified as overloaded. In this work, we introduce a Bayesian Belief Network which learns over time which of the virtual machines are best removed from a host that has been identified as overloaded. The probabilistic choice is made among virtual machines that are grouped by their degree of CPU usage. Given the feedback in the form of the computing resources saved, the system learns which virtual machine profiles should be shifted for best performance. This strategy compares favourably to two existing methods for load balancing.

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Cited By

Prajapati RJoshi HDegadwala S(2023)QoS Based Virtual Machine Consolidation for Energy Efficient and Economic Utilization of Cloud Resources2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)10.1109/ICSSAS57918.2023.10331674(951-957)Online publication date: 18-Oct-2023
https://doi.org/10.1109/ICSSAS57918.2023.10331674
Chen HWen YWang Y(2023)An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centersConcurrency and Computation: Practice and Experience10.1002/cpe.763635:9Online publication date: 3-Feb-2023
https://doi.org/10.1002/cpe.7636
Hasan W(2022)Virtual Machine Migration in Cloud ComputingOriental journal of computer science and technology10.13005/ojcst14.010203.0614:010203(46-51)Online publication date: 28-Feb-2022
https://doi.org/10.13005/ojcst14.010203.06
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cover image ACM Conferences

GREENS '16: Proceedings of the 5th International Workshop on Green and Sustainable Software

May 2016

32 pages

ISBN:9781450341615

DOI:10.1145/2896967

Copyright © 2016 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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ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

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Published: 14 May 2016

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ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

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Cited By

Prajapati RJoshi HDegadwala S(2023)QoS Based Virtual Machine Consolidation for Energy Efficient and Economic Utilization of Cloud Resources2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)10.1109/ICSSAS57918.2023.10331674(951-957)Online publication date: 18-Oct-2023
https://doi.org/10.1109/ICSSAS57918.2023.10331674
Chen HWen YWang Y(2023)An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centersConcurrency and Computation: Practice and Experience10.1002/cpe.763635:9Online publication date: 3-Feb-2023
https://doi.org/10.1002/cpe.7636
Hasan W(2022)Virtual Machine Migration in Cloud ComputingOriental journal of computer science and technology10.13005/ojcst14.010203.0614:010203(46-51)Online publication date: 28-Feb-2022
https://doi.org/10.13005/ojcst14.010203.06
Fernandez-Cerero DFernandez-Montes AJakobik A(2020)Limiting Global Warming by Improving Data-Centre SoftwareIEEE Access10.1109/ACCESS.2020.29783068(44048-44062)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2978306
Fernández-Cerero DOrtega-Irizo FFernández-Montes AVelasco-Morente F(2020)Bullfighting extreme scenarios in efficient hyper-scale cluster computingCluster Computing10.1007/s10586-020-03094-2Online publication date: 16-Mar-2020
https://doi.org/10.1007/s10586-020-03094-2
Singh PKhan BVidyarthi AHaes Alhelou HSiano P(2019)Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power FunctionApplied Sciences10.3390/app90714679:7(1467)Online publication date: 8-Apr-2019
https://doi.org/10.3390/app9071467
Ismail MHashim HYusof Y(2019)Stability control in virtual machine: Resource allocation for cloud computingAPPLIED PHYSICS OF CONDENSED MATTER (APCOM 2019)10.1063/1.5118150(020142)Online publication date: 2019
https://doi.org/10.1063/1.5118150
Ismail MRahmat MHashim HYusof Y(2019)Dynamics load balancing in virtual machine for cloud computingAPPLIED PHYSICS OF CONDENSED MATTER (APCOM 2019)10.1063/1.5118149(020141)Online publication date: 2019
https://doi.org/10.1063/1.5118149
Kilanioti IFernández-Montes AFernández-Cerero DKarageorgos AMettouris CNejkovic VAlbanis NBashroush RPapadopoulos G(2019)Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and ChallengesTarget Identification and Validation in Drug Discovery10.1007/978-3-030-16272-6_4(88-137)Online publication date: 26-Mar-2019
https://doi.org/10.1007/978-3-030-16272-6_4
Fernández-Cerero DFernández-Montes AVelasco F(2018)Productive Efficiency of Energy-Aware Data CentersEnergies10.3390/en1108205311:8(2053)Online publication date: 8-Aug-2018
https://doi.org/10.3390/en11082053
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