a resource allocation method for clusters of streaming media servers in multi-version VoD

A resource allocation method for clusters of streaming media servers in multi-version VoD is proposed to solve the problem of the inappropriate resources allocation for clusters of streaming media servers in large-scale mobile learning (m-learning) system. The method analyzes user's historical learning logs and mines user's behavior characteristics, and then it utilizes the queueing theory to establish a resource allocation model for the cluster of streaming media servers. The method uses the model to predict the user arrival rate in real-time and then allocates appropriate resources dynamically. Simulation results show the correctness and efficiency of the proposed method. The average service rejection rate and average resource utilization rate are around 1% and 80% respectively. The proposed method can ensure the user experience satisfaction and reduce service cost.