Multi-CDN as a Collective Service: Towards Hot Start in Congestion Control at Scale

Abstract

Modern CDN vendors typically provide multi-server services for content deliveries, in which different requests from the same client are often scheduled to diverse CDN nodes. In this case, the requested data (e.g., short video chunks) always suffer from cold start during its transmission, especially for the first flow of established connections, due to the unknown client-side network quality. Existing schemes focus on single-server startup parameter optimization, whose referred metrics appear to be extremely limited. To address this issue, we propose hStart, a novel collective service paradigm that enables hot-start congestion control by efficient collaboration between CDN nodes. hStart synchronizes probed network quality information (e.g., bandwidth, latency) across CDN nodes to initialize congestion control from historical data, avoiding probing convergence. It uses hierarchical state synchronization to limit overhead, and adaptive prediction with a time-decay trust model to handle dynamics. We implement and deploy the hStart system for the QUIC protocol. Extensive evaluations in both controlled testbeds and large-scale production CDN environments demonstrate that hStart effectively eliminates cold start penalties, boosting 46.47% throughput gain and 21.43% rebuffering reduction.