Energy-saving analysis of Cloud workload based on K-means clustering

Qingxin Xia; Yuqing Lan; Liang Zhao; Limin Xiao

doi:10.1109/ComComAp.2014.7017215

Energy-saving analysis of Cloud workload based on K-means clustering

Qingxin Xia
, Yuqing Lan
, Liang Zhao
, Limin Xiao

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

With the development of cloud infrastructure services, IaaS(Infrastructure as a Service) study on energy-saving technology has been attracted more and more attention. IaaS platform providers can provide high performance service for the users. Meanwhile, how to save the energy cost of the cloud platform must be considered without violating the Service Level Agreement(SLA). The overload and underload are two running statuses of physical machine(PM), the former will cause the possibility of SLA violation, while the latter will cause the low utilization rate of PM's resources, causing additional energy consumption. This paper proposes a model of workload characteristic based on K-means clustering analysis, using Google workload trace data set, which is the basis of virtual machine(VM) migrating when PM has been underloading or overloading. The establishment of workload characteristic model can present the demand of system resources in real time so that VM scheduling strategies carry out efficiently.

Original language	English
Title of host publication	Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014
Editors	Zhangbing Zhou, Jianwei Niu, Lei Shu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	305-309
Number of pages	5
ISBN (Electronic)	9781479948116
DOIs	https://doi.org/10.1109/ComComAp.2014.7017215
State	Published - 20 Jan 2014
Event	2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014 - Beijing, China Duration: 20 Oct 2014 → 22 Oct 2014

Publication series

Name	Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014

Conference

Conference	2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014
Country/Territory	China
City	Beijing
Period	20/10/14 → 22/10/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

IaaS
K-means
energy-saving
workload

Access to Document

10.1109/ComComAp.2014.7017215

Cite this

Xia, Q., Lan, Y., Zhao, L., & Xiao, L. (2014). Energy-saving analysis of Cloud workload based on K-means clustering. In Z. Zhou, J. Niu, & L. Shu (Eds.), Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014 (pp. 305-309). Article 7017215 (Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ComComAp.2014.7017215

Xia, Qingxin ; Lan, Yuqing ; Zhao, Liang et al. / Energy-saving analysis of Cloud workload based on K-means clustering. Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014. editor / Zhangbing Zhou ; Jianwei Niu ; Lei Shu. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 305-309 (Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014).

@inproceedings{7556252e515748aab1891fbc2d908f27,

title = "Energy-saving analysis of Cloud workload based on K-means clustering",

abstract = "With the development of cloud infrastructure services, IaaS(Infrastructure as a Service) study on energy-saving technology has been attracted more and more attention. IaaS platform providers can provide high performance service for the users. Meanwhile, how to save the energy cost of the cloud platform must be considered without violating the Service Level Agreement(SLA). The overload and underload are two running statuses of physical machine(PM), the former will cause the possibility of SLA violation, while the latter will cause the low utilization rate of PM's resources, causing additional energy consumption. This paper proposes a model of workload characteristic based on K-means clustering analysis, using Google workload trace data set, which is the basis of virtual machine(VM) migrating when PM has been underloading or overloading. The establishment of workload characteristic model can present the demand of system resources in real time so that VM scheduling strategies carry out efficiently.",

keywords = "IaaS, K-means, energy-saving, workload",

author = "Qingxin Xia and Yuqing Lan and Liang Zhao and Limin Xiao",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014 ; Conference date: 20-10-2014 Through 22-10-2014",

year = "2014",

month = jan,

day = "20",

doi = "10.1109/ComComAp.2014.7017215",

language = "英语",

series = "Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Xia, Q, Lan, Y, Zhao, L & Xiao, L 2014, Energy-saving analysis of Cloud workload based on K-means clustering. in Z Zhou, J Niu & L Shu (eds), Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014., 7017215, Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014, Institute of Electrical and Electronics Engineers Inc., pp. 305-309, 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014, Beijing, China, 20/10/14. https://doi.org/10.1109/ComComAp.2014.7017215

Energy-saving analysis of Cloud workload based on K-means clustering. / Xia, Qingxin; Lan, Yuqing; Zhao, Liang et al.
Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014. ed. / Zhangbing Zhou; Jianwei Niu; Lei Shu. Institute of Electrical and Electronics Engineers Inc., 2014. p. 305-309 7017215 (Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Energy-saving analysis of Cloud workload based on K-means clustering

AU - Xia, Qingxin

AU - Lan, Yuqing

AU - Zhao, Liang

AU - Xiao, Limin

PY - 2014/1/20

Y1 - 2014/1/20

N2 - With the development of cloud infrastructure services, IaaS(Infrastructure as a Service) study on energy-saving technology has been attracted more and more attention. IaaS platform providers can provide high performance service for the users. Meanwhile, how to save the energy cost of the cloud platform must be considered without violating the Service Level Agreement(SLA). The overload and underload are two running statuses of physical machine(PM), the former will cause the possibility of SLA violation, while the latter will cause the low utilization rate of PM's resources, causing additional energy consumption. This paper proposes a model of workload characteristic based on K-means clustering analysis, using Google workload trace data set, which is the basis of virtual machine(VM) migrating when PM has been underloading or overloading. The establishment of workload characteristic model can present the demand of system resources in real time so that VM scheduling strategies carry out efficiently.

AB - With the development of cloud infrastructure services, IaaS(Infrastructure as a Service) study on energy-saving technology has been attracted more and more attention. IaaS platform providers can provide high performance service for the users. Meanwhile, how to save the energy cost of the cloud platform must be considered without violating the Service Level Agreement(SLA). The overload and underload are two running statuses of physical machine(PM), the former will cause the possibility of SLA violation, while the latter will cause the low utilization rate of PM's resources, causing additional energy consumption. This paper proposes a model of workload characteristic based on K-means clustering analysis, using Google workload trace data set, which is the basis of virtual machine(VM) migrating when PM has been underloading or overloading. The establishment of workload characteristic model can present the demand of system resources in real time so that VM scheduling strategies carry out efficiently.

KW - IaaS

KW - K-means

KW - energy-saving

KW - workload

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SP - 305

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BT - Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014

A2 - Zhou, Zhangbing

A2 - Niu, Jianwei

A2 - Shu, Lei

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014

Y2 - 20 October 2014 through 22 October 2014

ER -

Xia Q, Lan Y, Zhao L, Xiao L. Energy-saving analysis of Cloud workload based on K-means clustering. In Zhou Z, Niu J, Shu L, editors, Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 305-309. 7017215. (Proceedings - 2014 IEEE Computers, Communications and IT Applications Conference, ComComAp 2014). doi: 10.1109/ComComAp.2014.7017215

Energy-saving analysis of Cloud workload based on K-means clustering

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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