Shared bottleneck detection based on trend line regression for multipath transmission

Abstract

SummaryCurrent proposed congestion control algorithms for multipath transmission couple all subflows together to maintain bottleneck fairness by assuming that the subflows traverse a common bottleneck. The coupled form congestion control algorithms restrict the multipath session to gain better throughput in non‐shared bottleneck situation. The throughput of a multipath session following independent rate control is larger than the throughput achieved by coupled form congestion control algorithm. The key point is to find a method to detect whether flows share a common bottleneck. If the subflows of a multipath session are detected out traversing a common bottleneck, the sender could perform coupled congestion control algorithm to guarantee bottleneck fairness and follow independent congestion control algorithm otherwise to improve throughput. Deduced from fluid model of additive increase multiplicative decrease (AIMD) rule, the delay samples can be linearly fitted when the bottleneck link falls into congestion. A delay trend line regression (TR) method is proposed for shared bottleneck detection. The delay slopes in close proximity indicate that the flows traverse the same bottleneck in great probability. Extensive simulations are performed to validate the effectiveness of TR algorithm in shared bottleneck detection. It is further implemented in multipath QUIC Golang codebase to work in collaboration with linked increases algorithm (LIA). Results show a multipath QUIC session with two subflows can gain higher throughput in non‐shared bottleneck situation and is less aggressive in shared bottleneck cases with the implementation of shared bottleneck detection algorithm.