An ensemble CPU load prediction algorithm using a Bayesian information criterion and smooth filters in a cloud computing environment

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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.

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