Maximizing real-time streaming services based on a multi-servers networking framework
In recent years, we have witnessed substantial exploitation of real-time streaming applications, such as video surveillance system on road crosses of a city. So far, real world applications mainly rely on the traditional well-known client-server and peer-to-peer schemes as the fundamental mechanism for communication. However, due to the limited resources on each terminal device in the applications, these two schemes cannot well leverage the processing capability between the source and destination of the video traffic, which leads to limited streaming services. For this reason, many QoS sensitive application cannot be supported in the real world. In this paper, we are motivated to address this problem by proposing a novel multi-server based framework. In this framework, multiple servers collaborate with each other to form a virtual server (also called cloud-server), and provide high-quality services such as real-time streams delivery and storage. Based on this framework, we further introduce a ( 1 - ? ) approximation algorithm to solve the NP-complete "maximum services"(MS) problem with the intention of handling large number of streaming flows originated by networks and maximizing the total number of services. Moreover, in order to backup the streaming data for later retrieval, based on the framework, an algorithm is proposed to implement backups and maximize streaming flows simultaneously. We conduct a series of experiments based on simulations to evaluate the performance of the newly proposed framework. We also compare our scheme to several traditional solutions. The results suggest that our proposed scheme significantly outperforms the traditional solutions.