Modified CUBIC Congestion Avoidance for Multi-side Parallel Downloading over Lossy Networks

Abstract

With the rapid growth of online video viewing, the quality of experience for users become a critical factor in the video streaming service to attract users and increase their adherence. Multi-side parallel downloading, which requests video segments from various low-cost data nodes simultaneously, could be a strategy to reduce the latency and improve the experience. However, the communication between such data nodes might not be as reliable as a conventional dedicated server. In such a network, the random loss events may bias the loss-based congestion control. Accordingly, this work incorporates a reevaluation mechanism into the CUBIC congestion avoidance state to correct the underestimation of the congestion window. In the experiment, we analyzed the round-trip time pattern and the transmission speed of CUBIC against our modification via network simulation with various loss rates. Our result shows that reevaluation helps to recover the congestion window and to improve the transmission speed in the extremely high-loss network. The derived insight may benefit the future improvement of loss-based congestion control. This paper presents one of the winning team's strategies in the MMSys23 Grand Challenge. The submitted code can be found on GitHub.1.