Resource Allocation for Virtual Streaming Media Server Cluster in Cloud-based Multi-version VoD

With the rapid development of mobile Internet and smart devices, VoD (video on demand) providers build media cloud to offer multi-bitrate video streaming services to users at a reduced cost, called as cloud-based multi-version VoD. In cloud-based multi-version VoD, we need to solve the problem of allocating appropriate resources for virtual streaming media server cluster with the aim of optimizing the user experience and reducing the service cost. To address this problem, a resource allocation for virtual streaming media server cluster in cloud-based multi-version VoD is proposed in this paper. We firstly analyze the user historical learning logs to mine the user behavior characteristics, including the average user request arrival rate, the video playing time distribution, and the video popularity distribution, etc. Then, based on the user behavior characteristics and the queueing theory, a resource allocation model for the virtual streaming media server cluster is introduced. It predicts the user arrival rate at first and then allocates appropriate resources dynamically to solve the resources allocation irrationality problem. Simulation results have proved the proposed method can allocate appropriate resources for virtual streaming media server cluster, which can ensure the user experience satisfaction and improve the resources utilization.