Journal of Advanced Computational Intelligence and Intelligent Informatics
Online ISSN : 1883-8014
Print ISSN : 1343-0130
ISSN-L : 1883-8014
Regular Papers
PSO-SVR-Based Resource Demand Prediction in Cloud Computing
Zhengfa ZhuJun PengZhuofu ZhouXiaoyong ZhangZhiwu Huang
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ジャーナル オープンアクセス

2016 年 20 巻 2 号 p. 324-331

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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).

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© 2016 Fuji Technology Press Ltd.

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