Cloud computing provides a new opportunity for Video Service Providers (VSP) to running compute-intensive video applications in a cost effective manner. Under this paradigm, a VSP may rent virtual machines (VMs) from multiple geo-distributed datacenters that are close to video requestors to run their services. As user demands are difficult to predict and the prices of the VMs vary in different time and region, optimizing the number of VMs of each type rented from datacenters located in different regions in a given time frame becomes essential to achieve cost effectiveness for VSPs. Meanwhile, it is equally important to guarantee users’ Quality of Experience (QoE) with rented VMs. In this paper, we give a systematic method called  Dy namical  Re quest Redire  c t i on and R e source Pro v  isioning ( DYRECEIVE ) to address this problem. We formulate the problem as a stochastic optimization problem and design a Lyapunov optimization framework based online algorithm to solve it. Our method is able to minimize the long-term time average cost of renting cloud resources while maintaining the user QoE. Theoretical analysis shows that our online algorithm can produce a solution within an upper bound to the optimal solution achieved through offline computing. Extensive experiments shows that our method is adaptive to request pattern changes along time and outperforms existing algorithms.
['Wenhua Xiao', 'Weidong Bao', 'Xiaomin Zhu', 'Chen Wang', 'Lidong Chen', 'Laurence T. Yang']
Dynamic Request Redirection and Resource Provisioning for Cloud-Based Video Services under Heterogeneous Environment