Cost-Effective Low-Delay Design for Multiparty Cloud Video Conferencing

Abstract

Multiparty cloud video conferencing architecture has been recently advocated to exploit rich computing and bandwidth resources in the cloud to effectively improve video conferencing performance. As a typical design in this architecture, multiple agents , i.e., virtual machines, are deployed in different cloud sites, and users are assigned to the agents. Then, the users communicate through the agents, and the agents might transcode the recorded videos given the heterogeneities among devices in terms of hardware specification and connectivity. In this architecture, two critical and nontrivial challenges are: 1) assigning users to agents to reduce the operational cost and the user-to-user conferencing delay and 2) identifying best agents to perform transcoding tasks, taking into account the heterogeneous bandwidth and processing availabilities. To address these challenges, we cast a joint problem of user-to-agent assignment and transcoding-agent selection. The ultimate objective is to simultaneously minimize the cost of the service provider and the conferencing delay. The problem is combinatorial in nature, which belongs to the NP-hard node assignment problems. We leverage the Markov approximation framework and devise an adaptive parallel algorithm that finds a close-to-optimal solution to our problem with a bounded performance guarantee. To evaluate the performance of our solution, we implement a prototype video conferencing system and carry out trace-driven experiments. In a set of large-scale experiments using PlanetLab traces, our solution decreases the operational cost by $77\%$ and simultaneously yields lower conferencing delay compared with an existing alternative.