Scalable Real-Time Bandwidth Fairness in Switches

Network operators want to enforce fair bandwidth sharing between users without solely relying on congestion control running on end-user devices. However, in edge networks (e.g., 5G), the number of user devices sharing a bottleneck link far exceeds the number of queues supported by today's switch hardware; even accurately tracking per-user sending rates may become too resource-intensive. Meanwhile, traditional software-based queuing on CPUs struggles to meet the high throughput and low latency demanded by 5G users. We propose (), a per-user bandwidth limit enforcer that runs fully in the data plane of commodity switches. tracks each user's approximate traffic rate and compares it against a bandwidth limit, which is iteratively updated via a real-time feedback loop to achieve max-min fairness across users. Using a novel sketch data structure, avoids storing per-user state, and therefore scales to thousands of slices and millions of users. Furthermore, supports network slicing, where each slice has a guaranteed share of the bandwidth that can be scavenged by other slices when under-utilized. Evaluation shows can achieve fair bandwidth allocation within 3.1ms, 13x faster than prior data-plane hierarchical schedulers.