CAREER: Achieving Self-Tunability of Peer-to-Peer Streaming Service through User-Level QoS Inference

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This project aims to build a self-tuning multimedia streaming service that optimizes the user viewing experience by analyzing user behavior, inferring personalized quality of service (QoS) requirements, and tuning system and network resources to maximize utilities of all users. The advent of peer-to-peer (P2P) technology has provided the ideal playground for this research. Traces from operating P2P streaming systems provide rich datasets with which to study the influence of QoS factors on users' viewing experience. In addition, the affordability of a P2P streaming testbed provides both the sandbox to test research ideas and a general educational platform accessible to a large Internet user base.This CAREER project has two complementary tracks: use-level QoS modeling and QoS-aware resource allocation. On the modeling track, the user utility function is formulated using statistical tools such as regression analysis to infer the influence of individual network- and system-level QoS factors on the user behaviors (e.g., skipping, changing channel, or premature leave), which reflect the subjective user viewing experience. On the resource allocation track, the problem is studied using a time-aware multi-dimensional optimization framework, which helps address the unique challenges posed by the semantics of a P2P system, e.g., how to inclusively optimize the resource allocation under heterogeneous user utility functions. Broader Impact:This research plan is expected to incubate the emergence of an extremely cost-effective and scalable P2P service platform as the innovation hotbed to significantly lower the entry barrier for new media-rich applications. The analytical outcome from user behavior study will also bring strategic insights and design guidelines applicable to all multimedia service paradigms beyond P2P.