CPUusage prediction for cloud resource provisioning based on deep belief network and particle swarm optimization

Resource usage prediction is increasingly important in cloud computing environments, and CPU usage prediction is especially helpful for improving the efficiency of resource provisioning and reducing energy consumption of cloud datacenters. However, accurate CPU usage prediction remains a challenge and few works have been done on predicting CPU usage of physical machines in cloud datacenters. In this article, we present a deep belief network (DBN) and particle swarm optimization (PSO) based CPU usage prediction algorithm, which is named DP-CUPA and aimed to provide more accurate prediction results. The DP-CUPA consists of three main steps. First, the historic data on CPU usage are preprocessed and normalized. Then, the autoregressive model and grey model are adopted as base prediction models and trained to provide extra input information for training DBN. Finally, the PSO is used to estimate DBN parameters and the DBN neural network is trained to predict CPU usage. The effectiveness of the DP-CUPA is evaluated by extensive experiments with a real-world dataset of Google cluster usage trace.