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Optimizing Power Consumption in Cloud Computing based on Optimization and Predictive Analysis

Authors:

Sungyoung LeeAuthors Info & Claims

IMCOM '18: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication

Article No.: 93, Pages 1 - 6

https://doi.org/10.1145/3164541.3164608

Published: 05 January 2018 Publication History

Abstract

Due to the budget and the environmental issues, achieving energy efficiency gradually receives a lot of attentions these days. In our previous research, a prediction technique has been developed to improve the monitoring statistics. In this research, by adopting the predictive monitoring information, our new proposal can perform the optimization to solve the energy issue of cloud computing. Actually, the optimization technique, which is convex optimization, is coupled with the proposed prediction method to produce a near-optimal set of hosting physical machines. After that, a corresponding migrating instruction can be created eventually. Based on this instruction, the cloud orchestrator can suitably relocate virtual machines to a designed subset of infrastructure. Subsequently, the idle physical servers can be turned off in an appropriate manner to save the power as well as maintain the system performance. For the purpose of evaluation, an experiment is conducted based on 29-day period of Google traces. By utilizing this evaluation, the proposed approach shows the potential to significantly reduce the power consumption without affecting the quality of services.

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Cited By

Dias ACorreia LMalheiros N(2021)A Systematic Literature Review on Virtual Machine ConsolidationACM Computing Surveys10.1145/347097254:8(1-38)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3470972
Głąbowski MSobieraj MStasiak MDominik Stasiak M(2020)Modeling of Clos Switching Structures with Dynamically Variable Number of Active Switches in the Spine StageElectronics10.3390/electronics90710739:7(1073)Online publication date: 30-Jun-2020
https://doi.org/10.3390/electronics9071073
Meneguette RBoukerche A(2020)An Efficient Green-Aware Architecture for Virtual Machine Migration in Sustainable Vehicular CloudsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2019.29046725:1(25-36)Online publication date: 1-Jan-2020
https://doi.org/10.1109/TSUSC.2019.2904672
Show More Cited By

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Optimizing Power Consumption in Cloud Computing based on Optimization and Predictive Analysis

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cover image ACM Other conferences

IMCOM '18: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication

January 2018

628 pages

ISBN:9781450363853

DOI:10.1145/3164541

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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SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

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Published: 05 January 2018

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IMCOM '18

IMCOM '18: The 12th International Conference on Ubiquitous Information Management and Communication

January 5 - 7, 2018

Langkawi, Malaysia

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IMCOM '18 Paper Acceptance Rate 100 of 255 submissions, 39%;

Overall Acceptance Rate 213 of 621 submissions, 34%

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Cited By

Dias ACorreia LMalheiros N(2021)A Systematic Literature Review on Virtual Machine ConsolidationACM Computing Surveys10.1145/347097254:8(1-38)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3470972
Głąbowski MSobieraj MStasiak MDominik Stasiak M(2020)Modeling of Clos Switching Structures with Dynamically Variable Number of Active Switches in the Spine StageElectronics10.3390/electronics90710739:7(1073)Online publication date: 30-Jun-2020
https://doi.org/10.3390/electronics9071073
Meneguette RBoukerche A(2020)An Efficient Green-Aware Architecture for Virtual Machine Migration in Sustainable Vehicular CloudsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2019.29046725:1(25-36)Online publication date: 1-Jan-2020
https://doi.org/10.1109/TSUSC.2019.2904672
Meneguette RBoukerche A(2019)Vehicular Clouds Leveraging Mobile Urban Computing Through Resource DiscoveryIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.2939249(1-8)Online publication date: 2019
https://doi.org/10.1109/TITS.2019.2939249
Meneguette RRodrigues Dda Costa JRosario DVillas L(2019)A Virtual Machine Migration Policy Based on Multiple Attribute Decision in Vehicular Cloud ScenarioICC 2019 - 2019 IEEE International Conference on Communications (ICC)10.1109/ICC.2019.8761248(1-6)Online publication date: May-2019
https://doi.org/10.1109/ICC.2019.8761248

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