LP-WRR: Towards Adaptive Performance-Aware Load Balancing

Abstract

Load balancers in practice often rely on fixed heuristics such as weighted round-robin (WRR) or least connection (LC). Although these methods scale well, they do not capture differences in backend service capacity or runtime performance variations. which can increase tail latency and request drop rates in shared clusters. This issue becomes particularly significant for modern cloud inference workloads such as large language model (LLM) serving, where request processing times and backend utilization can vary substantially. To address this issue, we propose LP-WRR, an external control-plane mechanism that adjusts weighted round-robin weights using latency feedback. Our method periodically solves a constrained linear program (LP) to reduce expected service latency, then smooths the resulting weights with an exponential moving average so that updates do not fluctuate excessively, all while keeping the optimization logic off the data path. We then evaluate LP-WRR in a simulator for shared latency-sensitive services, using workloads derived from seven days of Azure ChatGPT traces as a representative inference workload, and compare it with the original load-balancing algorithm. Across the heterogeneity scenarios at low workload, our method improves both mean and tail latency, reducing p95 latency by $\mathbf{2 4. 7 \%}$ on average compared to baseline policies. Under the highest tested load, it also lowers the system-wide drop ratio in both homogeneous and heterogeneous infrastructure.