Parallel processing of streaming media on heterogeneous hosts using work stealing

Abstract

Along with the dramatic development of streaming media applications, media streaming faces many challenges, such as format complexity, large volume and high requirements on quality-of-service (QoS). All of them make streaming media applications computation-intensive.Existing architecture-based approaches, like enhancing the computing power of single computer or organizing computer clusters, have the common disadvantage that they highly rely on the development of the physical equipments, either single computer's hardware or network infrastructure.Instead, software-based approches are currently carried out by applying parallelism to media processing. This thesis proposes a parallel media-processing system that applies functional parallelism to a heterogeneous environment by exploiting diverse general-purpose computers on networks. Our experimental results demonstrate that our system achieves impressive efficiency and robustness. It illustrates that functional parallelism with proper task size is an effective solution to reduce the computation bottleneck in a streaming media processing system.