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Research on CPU Workload Prediction and Balancing in Cloud Environment

Servers workload in the cloud environment should be balanced in order to achieve high efficiency and reduce resources consuming. One of the solutions is based on workload prediction, and design a proper load migration and balancing strategy. For the ease of discussion, we focus on CPU workload only in this paper. Specifically, considering the characteristics of workload, such as the strong correlation with time, we employ a time-series based two-step method to predict the CPU workload for both individual physical server and the cluster. Then, with the knowledge of the cluster workload, we design a strategy for workload migration and load balancing. Besides, we conduct extensive experiments to evaluate our method.