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Resource requests prediction in the cloud computing environment with a deep belief network
Author(s) -
Zhang Weishan,
Duan Pengcheng,
Yang Laurence T,
Xia Feng,
Li Zhongwei,
Lu Qinghua,
Gong Wenjuan,
Yang Su
Publication year - 2017
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2426
Subject(s) - cloud computing , computer science , deep belief network , variance (accounting) , artificial intelligence , set (abstract data type) , data mining , resource (disambiguation) , machine learning , scheduling (production processes) , deep learning , mean squared error , mathematical optimization , statistics , mathematics , computer network , accounting , business , programming language , operating system
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.