Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center

Yanxin Liu; Jian Dong; Decheng Zuo; Hongwei Liu

doi:10.1109/QRS.2019.00036

Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center

School of Computer Science and Technology, Harbin Institute of Technology

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

2 Scopus citations

Abstract

Due to the increasing scale of cloud data center, the issue of energy consumption is becoming pretty significant. To tackle this problem, an extremely effective approach is increasing the utilization of resource in data center. Researchers have found that accurate load prediction can help allocator distribute resource reasonably, so as to increase the utilization. There are a lot of traditional prediction algorithms which have been applied to cloud data center, such as linear regression. However, with the development of technologies, a number of novel prediction algorithms are brought out, for example, neural network. This paper assesses and analyzes the performance of several different prediction algorithms applying on data sets from real world. We get some meaningful and interesting conclusions from comparison among these algorithms, which may offer references for system designers of cloud data center.

Original language	English
Title of host publication	Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	197-203
Number of pages	7
ISBN (Electronic)	9781728139272
DOIs	https://doi.org/10.1109/QRS.2019.00036
State	Published - Jul 2019
Externally published	Yes
Event	19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019 - Sofia, Bulgaria Duration: 22 Jul 2019 → 26 Jul 2019

Publication series

Name	Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019

Conference

Conference	19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019
Country/Territory	Bulgaria
City	Sofia
Period	22/07/19 → 26/07/19

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

cloud data center
comparison
linear regression
load prediction
neural network

Access to Document

10.1109/QRS.2019.00036

Cite this

Liu, Y., Dong, J., Zuo, D., & Liu, H. (2019). Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center. In Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019 (pp. 197-203). Article 8854686 (Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QRS.2019.00036

Liu, Yanxin ; Dong, Jian ; Zuo, Decheng et al. / Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center. Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 197-203 (Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019).

@inproceedings{1819961a71ce4fa996de63c9a933b769,

title = "Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center",

abstract = "Due to the increasing scale of cloud data center, the issue of energy consumption is becoming pretty significant. To tackle this problem, an extremely effective approach is increasing the utilization of resource in data center. Researchers have found that accurate load prediction can help allocator distribute resource reasonably, so as to increase the utilization. There are a lot of traditional prediction algorithms which have been applied to cloud data center, such as linear regression. However, with the development of technologies, a number of novel prediction algorithms are brought out, for example, neural network. This paper assesses and analyzes the performance of several different prediction algorithms applying on data sets from real world. We get some meaningful and interesting conclusions from comparison among these algorithms, which may offer references for system designers of cloud data center.",

keywords = "cloud data center, comparison, linear regression, load prediction, neural network",

author = "Yanxin Liu and Jian Dong and Decheng Zuo and Hongwei Liu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019 ; Conference date: 22-07-2019 Through 26-07-2019",

year = "2019",

month = jul,

doi = "10.1109/QRS.2019.00036",

language = "英语",

series = "Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019",

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

pages = "197--203",

booktitle = "Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019",

address = "美国",

}

Liu, Y, Dong, J , Zuo, D & Liu, H 2019, Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center. in Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019., 8854686, Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 197-203, 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019, Sofia, Bulgaria, 22/07/19. https://doi.org/10.1109/QRS.2019.00036

Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center. / Liu, Yanxin; Dong, Jian ; Zuo, Decheng et al.
Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 197-203 8854686 (Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019).

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

TY - GEN

T1 - Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center

AU - Liu, Yanxin

AU - Dong, Jian

AU - Zuo, Decheng

AU - Liu, Hongwei

PY - 2019/7

Y1 - 2019/7

N2 - Due to the increasing scale of cloud data center, the issue of energy consumption is becoming pretty significant. To tackle this problem, an extremely effective approach is increasing the utilization of resource in data center. Researchers have found that accurate load prediction can help allocator distribute resource reasonably, so as to increase the utilization. There are a lot of traditional prediction algorithms which have been applied to cloud data center, such as linear regression. However, with the development of technologies, a number of novel prediction algorithms are brought out, for example, neural network. This paper assesses and analyzes the performance of several different prediction algorithms applying on data sets from real world. We get some meaningful and interesting conclusions from comparison among these algorithms, which may offer references for system designers of cloud data center.

AB - Due to the increasing scale of cloud data center, the issue of energy consumption is becoming pretty significant. To tackle this problem, an extremely effective approach is increasing the utilization of resource in data center. Researchers have found that accurate load prediction can help allocator distribute resource reasonably, so as to increase the utilization. There are a lot of traditional prediction algorithms which have been applied to cloud data center, such as linear regression. However, with the development of technologies, a number of novel prediction algorithms are brought out, for example, neural network. This paper assesses and analyzes the performance of several different prediction algorithms applying on data sets from real world. We get some meaningful and interesting conclusions from comparison among these algorithms, which may offer references for system designers of cloud data center.

KW - cloud data center

KW - comparison

KW - linear regression

KW - load prediction

KW - neural network

UR - https://www.scopus.com/pages/publications/85073795672

U2 - 10.1109/QRS.2019.00036

DO - 10.1109/QRS.2019.00036

M3 - 会议稿件

AN - SCOPUS:85073795672

T3 - Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019

SP - 197

EP - 203

BT - Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019

Y2 - 22 July 2019 through 26 July 2019

ER -

Liu Y, Dong J , Zuo D , Liu H. Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center. In Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 197-203. 8854686. (Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019). doi: 10.1109/QRS.2019.00036

Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this