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JACIII Vol.20 No.2 pp. 324-331
doi: 10.20965/jaciii.2016.p0324
(2016)

Paper:

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PSO-SVR-Based Resource Demand Prediction in Cloud Computing

Zhengfa Zhu, Jun Peng, Zhuofu Zhou, Xiaoyong Zhang, and Zhiwu Huang

School of Information Science and Engineering, Central South University
Changsha, Hunan 410075, China

Received:
November 10, 2015
Accepted:
December 10, 2015
Online released:
March 18, 2016
Published:
March 20, 2016
Creative Commons License
Keywords:
cloud computing, resource usage prediction, support vector regression, particle swarm optimization
Abstract
The essential of cloud computing is to offer elastic resources (such as CPU, memory, storage, and more) allocation to cloud customers on demand, and the resources are allocated dynamically in a pay-as-you-go fashion. In order to achieve this goal automatically while guaranteeing the performance of the application deployed in the cloud, a proactive resource scaling strategy is necessary for cloud providers. In this paper, we present an optimal resource usage prediction approach based on Support Vector Regression (SVR) that predicts resource demands from users in the near future. In order to improve the forecasting accuracy, Particle Swarm Optimization (PSO) is integrated in the model selection process for SVR to optimize the parameters of the model. Experiment results show that the prediction model achieves high accuracy and outperforms traditional SVR and Linear Regression (LR).
Cite this article as:
Z. Zhu, J. Peng, Z. Zhou, X. Zhang, and Z. Huang, “PSO-SVR-Based Resource Demand Prediction in Cloud Computing,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.2, pp. 324-331, 2016.
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