Oceanus: Scheduling Traffic Flows to Achieve Cost-Efficiency under Uncertainties in Large-Scale Edge CDNs
Chuanqing Lin, Gerui Lv, Fuhua Zeng, Hanlin Yang, Junwei Li, Xiaodong Li, Jingyu Yang, Yu Tian, Qinghua Wu, Zhenyu Li, Gaogang Xie · Proceedings of the ACM on Networking · 2025
Large-scale edge Content Delivery Networks (CDNs) provide low-latency content access services and suffer from high bandwidth costs. While previous studies have sought to optimize bandwidth costs under percentile billing, the efficacy is compromised due to the pervasive uncertainty inherent in practical systems, including traffic demand dynamics, performance-constrained scheduling bias, and systemic scheduling deviations. Such uncertainties can result in large gaps among optimal, expected, and actual utilization of massive vulnerable and heterogeneous edge nodes. To address these uncertainties, we propose Oceanus, a cost-effective traffic scheduling system for large-scale edge CDN systems. Oceanus decouples the bandwidth planning problem and performs on multiple timescales. In addition, Oceanus coordinates bandwidth planning with flow scheduling through the bidirectional feedback scheme. Oceanus further utilizes nodes with minimal marginal cost to reduce additional bandwidth cost. Extensive experiments in a trace-driven testbed and real-world deployment confirm the effectiveness of Oceanus. Compared to the state-of-the-art scheduling method, Oceanus achieves 79.4% (vs. 51.5%) of optimum cost reduction and reduces 21.4% (vs. 8.1%) bandwidth costs.