Kaixi Zhang

Dingyan Zhang, Jinbo Han, Kaixi Zhang et al.

High-quality LLM request scheduling requires achieving two key objectives: whether the routed instance has KV$ to accelerate the request execution and whether the workload is balanced across instances. Achieving both objectives is challenging because pursuing one objective may compromise the other. Current approaches adopt various combinators (e.g., linear combinations) to compute a scheduling score combining indicators for the two objectives, which are complex in that they either require significant workload-specific hyperparameter tuning or model-hardware-aware simulator development, and could still lead to suboptimal performance. In this paper, we show that using a simple multiplication of two carefully chosen indicators-one for KV$-aware (new prefill tokens if routed to an instance) and one for load balancing-aware (current batch size of the instance)-as the scheduling score can simultaneously achieve both objectives well without any hyperparameter tuning. The key idea is that the multiplied score considers both objectives in a manner similar to a linear combination, with a nice property that the original hyperparameters are canceled out during comparison so we don't need tuning to find the best parameters. The two indicators are chosen based on our analysis of LLM characteristics, and our extensive experiments show that this simple approach can reduce TTFT by 92% and 52%, and TPOT by 21% and 20%, compared to vLLM-v1 and a production scheduler on real-world workloads covering chatbots, API calls, and coding agents. We also mathematically derive the conditions under which multiplication may fail, and find that such conditions are extremely rare in practice and can be detected (and mitigated) beforehand.

Jiahao Wang, Kaizhan Lin, Kaixi Zhang et al.

LLM scheduling is critical to serving, yet it remains unclear how well existing designs fit agentic serving--with LLM requests issued by agents instead of humans. This shifts the workload in two ways: (1) agents act only on complete responses, making the cluster's tokens per second (TPS) the primary goal and relaxing--not eliminating--per-token latency requirements; and (2) requests share much of their KV\$-reuse exceeds 80% of request tokens in a production trace from BAILIAN, versus 54-62% in chat. This paper first contributes a systematic study of request scheduling for agents on two real-world traces. We find that to increase KV\$ reuse, existing schedulers overly prioritize routing requests to instances caching their KV\$, overloading a few while leaving the rest idle, capping TPS. We thus present two key insights: (1) load balance need not sacrifice all KV\$ reuse, thanks to the global-tier KV\$ store and (2) by utilizing the workload's intra-session locality, balancing a small fraction of requests--the first request in each agent session--suffices to balance the cluster without sacrificing most KV\$ reuse on local instances. SMETRIC realizes these insights with balanced session-centric scheduling: it routes each session's first request purely for load balance and its follow-up requests in a cache-aware manner, preserving load balance and local reuse while keeping demand on the global tier low. Using the session turn information as the scheduling metric is deliberate: it is derived efficiently and accurately from the user inputs alone, so the scheduler stays clean and stateless. SMETRIC improves cluster TPS by 10-16% under prefill-decode colocation with a global store and prefill TPS by 2-34% under disaggregation over state-of-the-art schedulers, also with a better per-token latency.