Learning Production-Optimized Congestion Control Selection for Alibaba Cloud CDN

Today's content delivery networks (CDNs) typically use static congestion control (CC) configurations, yet the diverse network environments preclude a universally optimal CC for all geographical regions, as evidenced by our extensive measurements. Current CC algorithms, limited by narrow applicability or high maintenance costs, struggle in large-scale CDNs. This work introduces ALICCS, the first CC Selection (CCS) approach tailored for production CDN, integrating fine-grained domain knowledge for learning to choose the best CC from existing, well-established ones. Through an over-one-year real-world deployment in Alibaba Cloud CDN, ALICCS has enhanced the Quality-of-Experience (QoE) by up to 9.31%, surpassing the competitive margin in the CDN market, and significantly reduced the retransmission rate by 25.51% to 174.36% across all provinces of China, leading to cost savings over 10 million US dollars. We also share key insights and experiences from deploying ALICCS at scale, highlighting traffic patterns in Alibaba Cloud CDN.