This paper evaluates QUIC deployment and performance through active measurements of 1,572 Chinese and 1,953 non-Chinese websites. Results show clear regional and categorical differences: HTTP/3 support is 35.0% for non-Chinese websites but only 5.7% for Chinese websites. Performance gains also depend on network conditions, reducing transfer time by 17.06% in Chinese long-tail networks while slightly regressing by 0.79% in optimized low-latency CDN edge scenarios. Overall, QUIC’s deployment and benefits are highly scenario-dependent.

Modern clusters rely on centralized control planes, but decisions can be slow and fragile under control-plane degradations. We present Reflex, a bi-modal recovery design for clusters under control-plane degradation. Reflex adds a reflex-arc-like path that makes rapid takeover decisions from preprocessed local priorities while suppressing contention via lightweight coordination. After services are runnable, it performs steady-state reconstruction. Our evaluation shows bounded latency and robust conflict suppression under control-plane degradation and bursty failures.

Due to the limited permissions for upgrading dual-side (i.e., server-side and client-side) loss tolerance schemes from the perspective of CDN vendors in a multi-supplier market, modern large-scale live streaming services are still using the automatic-repeat-request (ARQ) based paradigm for loss recovery, which only requires server-side modifications. In this paper, we first conduct a large-scale measurement study with up to 50 million live streams. We find that loss showsdynamicsand live streaming contains frequenton-off mode switchingin the wild. We further find that the recovery latency, enlarged by the ubiquitous retransmission loss, is a critical factor affecting live streaming’s client-side QoE (e.g., video freezing). We then propose an enhanced recovery mechanism called AutoRec, which can transform the disadvantages of on-off mode switching into an advantage for reducing loss recovery latency without any modifications on the client side. AutoRec allows users to customize overhead tolerance and recovery latency tolerance and adaptively adjusts strategies as the network environment changes to ensure that recovery latency meets user demands whenever possible while keeping overhead under control. We implement AutoRec upon QUIC and evaluate it via testbed and real-world commercial services deployments. The experimental results demonstrate the practicability and profitability of AutoRec.