Towards Truly Burst-Aware Evaluation of Data Center Congestion Control
The performance of datacenter congestion control algorithms (CCAs) is highly sensitive to bursty traffic patterns, yet a significant fidelity gap exists between evaluation workloads and production traffic. Current evaluations primarily rely on synthetic workloads constructed from flow-size CDFs with incast overlaid on top, an approach that, while intuitive, we show produces traffic that is dissimilar to production in its temporal burst clustering. As a result, these workloads fail to exercise the full range of conditions that CCAs encounter in production, and hence, protocols that demonstrate gains in simulation risk diminished performance or unexpected failure modes upon deployment. This motivates the need for a deeper understanding of burstiness for CCA evaluation. To this end, we decompose burstiness into four key dimensions, and use DCTCP as a case study to show distinct behavioral regimes in each dimension. Building on this, we envision a burst-centric evaluation stack: behavioral regime analysis across various CCA classes, and a burst generator to ensure regime coverage along these dimensions, enabling thorough and robust evaluations.