BEACON: Benchmarking Adaptability of Hardware-Offloaded Congestion Control Algorithms

Abstract

Modern AI workloads demand microsecond-scale network reaction times, forcing data centers to offload congestion control algorithms (CCAs) to hardware. Simultaneously, emerging transport standards introduce diverse congestion signals like delay, CSIG, INT, and packet trimming. Understanding hardware-offloaded CCA adaptability—the speed of rate reduction and recovery—is critical, yet existing benchmarking methods force a trade-off between fidelity and flexibility. Commercial testers lack congestion signal flexibility, while software simulators fail to capture line-rate hardware behaviors. To resolve this tension, we present BEACON, a high-fidelity evaluation framework designed to benchmark adaptability of hardware-offloaded CCA. By operating as an inline, programmable data-plane middleman, BEACON reproduces authentic hardware interactions at full line rate while maintaining software-like flexibility for emerging signals. A preliminary evaluation on a testbed with 100 Gbps RoCEv2-based RNICs and Tofino demonstrate that BEACON precisely emulates sub-μs congestion windows and captures transient reaction times with negligible overhead, laying the foundation for a comprehensive benchmarking of hardware-offloaded CCAs.