Reducing tail latency for multi-bottleneck in datacenter networks: A compound approach

The effectiveness of network congestion control fundamentally depends on the accuracy and granularity of congestion feedback . In datacenter networks, precise feedback is essential for achieving high performance. Most existing approaches use either Explicit Congestion Notification (ECN) or network delay (e.g., RTT) independently as congestion indicators . However, in multi-bottleneck networks, the limitations of these signals become more pronounced: ECN struggles with large cumulative end-to-end latency, while RTT lacks the precision needed to control queuing delays at individual hops. To address these challenges, we propose Cocktail , a simple yet effective transport protocol for datacenter networks that combines both ECN and RTT congestion signals to more effectively handle multi-bottleneck scenarios. By leveraging the ECN signal, Cocktail bounds per-hop queue lengths, enhancing its ability to control single-hop latency and prevent packet loss . Additionally, by estimating RTT, Cocktail effectively manages end-to-end delay, resulting in lower Flow Completion Time (FCT). Extensive experimental evaluations in Mininet demonstrate that Cocktail significantly reduces the average and 99th-percentile completion times for small flows by up to 20% and 29%, respectively, compared to current practices under production workloads.