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An ensemble CPU load prediction algorithm using a Bayesian information criterion and smooth filters in a cloud computing environment
Author(s) -
Tofighy Sajjad,
Rahmanian Ali A.,
GhobaeiArani Mostafa
Publication year - 2018
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.2641
Subject(s) - cloud computing , computer science , workload , data mining , resource (disambiguation) , resource management (computing) , set (abstract data type) , outlier , resource allocation , bayesian probability , data set , distributed computing , algorithm , machine learning , database , artificial intelligence , operating system , computer network , programming language
Summary Cloud resource management requires complex policies and decisions to ensure the suitable use of computing resources due to fluctuations in the demanding workload. Deciding the right amount of resource usage for performing user requests in cloud environments is not trivial. Therefore, an efficient resource prediction model can play important roles in cloud resource management to estimate the needed resources properly. In this paper, we propose an ensemble CPU load prediction model using a Bayesian information criterion to choose the best constituent model in each time slot based on the cloud resource usage history. Further, we apply a couple of smooth filters in order to decrease the negative impacts of outliers in the observed data points. We also present a framework for cloud resource management including a prediction module to estimate the resource usage more accurately. The experimental results on the data set of the CoMon project indicate that the proposed approach achieves higher accuracy compared with the other ensemble prediction algorithms.