Resource requests prediction in the cloud computing environment with a deep belief network. (18th July 2016)

• Record Type: Journal Article
• Authors: Zhang, Weishan; Duan, Pengcheng; Yang, Laurence T; Xia, Feng; Li, Zhongwei; Lu, Qinghua; Gong, Wenjuan; Yang, Su
• Other Names: Ranjan Rajiv guestEditor.; Wang Lizhe guestEditor.; Jayaraman Prem Prakash guestEditor.; Mitra Karan guestEditor.; Georgakopoulos Dimitrios guestEditor.
• Is Part Of: Software, practice & experience. Volume 47:Number 3(2017)
• Page Start: 473
• Page End: 488
• Publication Date: 2016-07-18
• Subjects: deep belief network -- prediction -- cloud computing -- resource request; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2426
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Accurate resource requests prediction is essential to achieve optimal job scheduling and load balancing for cloud Computing. Existing prediction approaches fall short in providing satisfactory accuracy because of high variances of cloud metrics. We propose a deep belief network (DBN)‐based approach to predict cloud resource requests. We design a set of experiments to find the most influential factors for prediction accuracy and the best DBN parameter set to achieve optimal performance. The innovative points of the proposed approach is that it introduces analysis of variance and orthogonal experimental design techniques into the parameter learning of DBN. The proposed approach achieves high accuracy with mean square error of [10 −6, 10 −5 ], approximately 72 % reduction compared with the traditional autoregressive integrated moving average predictor, and has better prediction accuracy compared with the state‐of‐art fractal modeling approach. Copyright © 2016 John Wiley & Sons, Ltd.