A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers

The rapid growth of computational power demand from scientific, business, and Web applications has led to the emergence of cloud-oriented data centers. These centers use pay-as-you-go execution environments that scale transparently to the user. Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment. Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective. Load classification before prediction is necessary to improve prediction accuracy. In this paper, a novel approach is proposed to forecast the future load for cloud-oriented data centers. First, a hidden Markov model (HMM) based data clustering method is adopted to classify the cloud load. The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers. Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load. With the data from this cluster, a genetic algorithm optimized Elman network is used to forecast future load. Experimental results show that our algorithm outperforms other approaches reported in previous works.