Locality-Aware Clustering Application Level Multicast for Live Streaming Services on the Internet

With the rapid growth of Internet, media streaming plays an important role for the growing demand of media service. Application Level Multicast (ALM) has emerged as a key alternative to enable broadcasting the streaming media over the Internet. Several studies have proposed solutions to construct ALM multicast trees with various aspects such as end-to-end delay optimization, quick joining delay, small maintenance join/leave overhead, and balance multicast tree. However, these approaches focus on the optimization of a simple measurement, end-to-end delay, while ignoring other factors such as bandwidth consumptions, which can be very critical for large-scale ALM. In addition, the tier-based nature of the Internet further increases the difficulties of constructing scalable ALM multicast tree. This paper proposes a hierarchical structure approach called Locality-Aware Clustering (LAC), which utilizes the knowledge of network topology to construct an efficient ALM multicast tree. Based on simulation studies, LAC algorithm can reduce the overlay stress by more than 80% comparing to traditional algorithms such as ZIGZAG and MBMT/MSMT while maintaining low overlay delay even when there are large number of nodes. This simulation results show that LAC algorithm can construct ALM multicast trees that are more localized, efficient and scalable than the traditional approaches.