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A power consumption model for cloud servers based on Elman Neural Network

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Wu, Wentai, Lin, Weiwei, He, Ligang, Wu, Guangxin and Hsu, Ching-Hsien (2021) A power consumption model for cloud servers based on Elman Neural Network. IEEE Transactions on Cloud Computing, 9 (4). pp. 1268-1277. doi:10.1109/TCC.2019.2922379

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Official URL: http://dx.doi.org/10.1109/TCC.2019.2922379

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Abstract

Leveraging power consumption models in software systems can achieve easy deployment of low-cost, high-availability power monitoring in cloud datacenters that are usually large-scale, heterogeneous and frequently scaling up. However, traditional regression-based power consumption models generally have two drawbacks. First, their mathematical forms are usually fixed and determined a priori. This may cause unacceptable increase of error or over-fitting as the power signatures of cloud servers are usually uncertain. Second, the characteristic of workload dispatched to cloud servers is constantly changing while regression-based models can hardly generalize to a wide range of servers and workload types. As a novel solution, we in this paper propose a server power consumption model based on Elman Neural Network (PCM-ENN), aiming to allow accurate and flexible power estimation. PCM-ENN is an end-to-end black box model capable of learning the temporal relation between samples in a time series of power consumption. We trained and evaluated PCM-ENN on two power sequence datasets collected from heterogeneous hardware and operating systems running quasi-production benchmarks like CloudSuite. Experimental result shows that PCM-ENN generated accurate estimates on server power consumption with only small errors, outperforming widely-used linear regression model and NARX model in terms of accuracy.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Neural networks (Computer science) , Cloud computing , Virtual computer systems
Journal or Publication Title: IEEE Transactions on Cloud Computing
Publisher: IEEE
ISSN: 2372-0018
Official Date: 1 October 2021
Dates:
DateEvent
1 October 2021Published
12 June 2019Available
8 June 2019Accepted
Volume: 9
Number: 4
Page Range: pp. 1268-1277
DOI: 10.1109/TCC.2019.2922379
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
61772205[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61872084[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
2017B010126002Guangdong University of Technologyhttp://dx.doi.org/10.13039/501100008326
2017B090901061Guangdong University of Technologyhttp://dx.doi.org/10.13039/501100008326
201802010010Guangzhou Science and Technology Program key projectshttp://dx.doi.org/10.13039/501100004000
201807010052Guangzhou Science and Technology Program key projectshttp://dx.doi.org/10.13039/501100004000
201907010001Guangzhou Science and Technology Program key projectshttp://dx.doi.org/10.13039/501100004000
201902010040Guangzhou Science and Technology Program key projectshttp://dx.doi.org/10.13039/501100004000
2017GJ001Hong Kong University of Science and Technologyhttp://dx.doi.org/10.13039/501100005950
Fundamental Research FundsSouth China University of Technologyhttp://dx.doi.org/10.13039/501100005015

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