Server load prediction algorithm based on CM-MC for cloud systems

doi:10.21629/JSEE.2018.05.17

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (5): 1069-1078.doi: 10.21629/JSEE.2018.05.17

• Software Algorithm and Simulation • Previous Articles Next Articles

Server load prediction algorithm based on CM-MC for cloud systems

Xiaolong XU^1,*(), Qitong ZHANG^1,²(), Yiqi MOU³(), Xinyuan LU⁴()

¹ Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
² State Key Laboratory of Information Security, Chinese Academy of Sciences, Beijing 100093, China
³ Shanghai Stock Exchange Technology Co., Ltd, Shanghai Stock Exchange, Shanghai 200120, China
⁴ Institute of Big Data Research at Yancheng, Nanjing University of Posts and Telecommunications, Yancheng 224000, China

Received:2017-09-11 Online:2018-10-26 Published:2018-11-14
Contact: Xiaolong XU E-mail:xuxl@njupt.edu.cn;1014041119@njupt.edu.cn;yqmou@sse.com.cn;1016041226@njupt.edu.cn
About author:XU Xiaolong was born in 1977. He received his B.S. degree in computer and its applications, M.S. degree in computer software and theories and Ph.D. degree in communications and information systems from Nanjing University of Posts & Telecommunications, Nanjing, China, in 1999, 2002 and 2008, respectively. He worked as a postdoctoral researcher at Station of Electronic Science and Technology, Nanjing University of Posts & Telecommunications from 2011 to 2013. He is currently a professor in College of Computer, Nanjing University of Posts & Telecommunications. He is a senior member of China Computer Federation. His current research interests include cloud computing, mobile computing, intelligent agent and information security. E-mail: xuxl@njupt.edu.cn|ZHANG Qitong was born in 1992. She received her B.E. degree in software engineering from Nanjing University of Posts & Telecommunications, Nanjing, China, in 2014. She is now a postgraduate student majoring in software engineering, and working in a project team supported by the State Key Laboratory of Information Security, Chinese Academy of Sciences, Beijing, China. Her current research interests include cloud computing and virtualization. E-mail: 1014041119@njupt.edu.cn|MOU Yiqi was born in 1989. He received his B.E. degree in network engineering from Nanjing University of Posts & Telecommunications, Nanjing, China, in 2011, and M.E. degree in computer technology from Nanjing University of Posts & Telecommunications, Nanjing, China, in 2016, respectively. He works as an engineer in Data Center Business Department at Shanghai Stock Exchange Technology Co., Ltd., Shanghai, China, carrying out research in cloud computing and data center network technology. E-mail: yqmou@sse.com.cn|LU Xinyuan was born in 1995. He received his B.E. degree in computer science and technology from Nanjing University of Posts & Telecommunications, Nanjing, Nanjing, China, in 2017. He works as an engineer in Institute of Big Data Research at Yancheng, Nanjing University of Posts and Telecommunications, Yancheng, China, carrying out research in cloud computing and data analysis. E-mail: 1016041226@njupt.edu.cn
Supported by:
the National Natural Science Foundation of China(61472192);the National Natural Science Foundation of China(61772286);the National Key Research and Development Program of China(2018YFB1003700);the Scientific and Technological Support Project (Society) of Jiangsu Province(BE2016776);the "333" Project of Jiangsu Province(BRA2017228);the "333" Project of Jiangsu Province(BRA2017401);This work was supported by the National Natural Science Foundation of China (61472192; 61772286), the National Key Research and Development Program of China (2018YFB1003700), the Scientific and Technological Support Project (Society) of Jiangsu Province (BE2016776), and the "333" Project of Jiangsu Province (BRA2017228; BRA2017401)

Abstract

Abstract:

Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service (QoS). This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model (CM) and the Markov chain (MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.

Key words: cloud computing, load prediction, cloud model, Markov chain, energy saving

Xiaolong XU, Qitong ZHANG, Yiqi MOU, Xinyuan LU. Server load prediction algorithm based on CM-MC for cloud systems[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 1069-1078.

Figures/Tables 8

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Interval	Ratio of the cloud drops to all cloud drops/%	Ratio of the contribution to all contributions/%
$[Ex-0.67En, Ex+0.67En]$	22.33	50
$[Ex-En, Ex+En]$	33.33	68.26
$[Ex-2En, Ex-En] {{{\rm{U}}}}$ $[Ex+En, Ex+2En]$	33.33	27.18
$[Ex-3En, Ex-2En]{{{\rm{U}}}}$ $[Ex+2En, Ex+3En]$	33.33	3

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Server load prediction algorithm based on CM-MC for cloud systems

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