A Deployment Optimization Scheme Over Multimedia Big Data for Large-Scale Media Streaming Application

Abstract

With the prosperity of media streaming applications over the Internet in the past decades, multimedia data has sharply increased (categorized as multimedia big data), which exerts more pressure on the infrastructure, such as networking of the application provider. In order to move this hurdle, an increasing number of traditional media streaming applications have migrated from a private server cluster onto the cloud. With the elastic resource provisioning and centralized management of the cloud, the operational costs of media streaming application providers can decrease dramatically. However, to the best of our knowledge, existing migration solutions do not fully take viewer information such as hardware condition into consideration. In this article, we consider the deployment optimization problem named ODP by leveraging local memories at each viewer. Considering the NP-hardness of calculating the optimal solution, we turn to propose computationally tractable algorithms. Specifically, we unfold the original problem into two interactive subproblems: coarse-grained migration subproblem and fine-grained scheduling subproblem. Then, the corresponding offline approximation algorithms with performance guarantee and computational efficiency are given. The results of extensive evaluation show that compared with the baseline algorithm without leveraging local memories at viewers, our proposed algorithms and their online versions can decrease total bandwidth reservation and enhance the utilization of bandwidth reservation dramatically.