Better End-to-End Adaptation Using Centralized Predictive Control

Transport layer and application layer of network stack use end-to-end adaptation protocols (e.g., TCP and bitrate-adaptive video) to achieve high performance by continuously adapting endpoint behavior to changes of network conditions. The traditional belief is that these protocols must be run independently by endpoints to achieve desirable performance. In essence, they use reactive logic triggered only by locally observable events. For instance, TCP reacts to a packet timeout by halving the congestion window. In this thesis, we argue that centralized predictive control can lead to better endto-end adaptation and large performance improvement at both transport layer and application layer. We show that it is feasible to decouple adaptation logics from endto-end adaptation protocols and centralize them into a global controller that makes predictive control using a global view of different connections’ performance. For instance, TCP with centralized predictive control can predict the best congestion window using other similar TCP sessions’ performance. To deliver the promised performance benefits of centralized predictive control, we must address two key technical challenges. First, we present prediction algorithms, which accurately predict the optimal adaptation behavior of endpoints by exploiting the structural information of the global view (e.g., some connections are subjected to same network bottleneck). Second, we present designs of a scalable control platform, which leverage the persistence of optimal decisions to minimize negative impacts of the inherent delay between the controller and widely distributed endpoints. This thesis will present algorithms and system designs of centralized predictive control for both transport layer and application layer. We show that our approach can lead to better performance for TCP, Internet video and real-time communication applications like Skype. Our preliminary experiments have shown significant improvement of Internet video quality by centralized predictive control. June 25, 2015 Thesis Proposal