The Library
A power consumption model for cloud servers based on Elman Neural Network
Tools
Tools
Tools
Wu, Wentai, Lin, Weiwei, He, Ligang, Wu, Guangxin and Hsu, Ching-Hsien (2019) A power consumption model for cloud servers based on Elman Neural Network. IEEE Transactions on Cloud Computing . doi:10.1109/TCC.2019.2922379 (In Press)
|
PDF
WRAP-power-consumption-model-cloud-servers-based-Elman-Neural-Network-He-2019.pdf - Accepted Version - Requires a PDF viewer. Download (2231Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TCC.2019.2922379
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: | 12 June 2019 | |||||||||||||||||||||||||||||||||
| Dates: |
|
|||||||||||||||||||||||||||||||||
| Date of first compliant deposit: | 2 August 2019 | |||||||||||||||||||||||||||||||||
| DOI: | 10.1109/TCC.2019.2922379 | |||||||||||||||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||
| Publication Status: | In Press | |||||||||||||||||||||||||||||||||
| 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: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
