SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
LLM scheduling is critical to serving, yet how well existing designs fit agentic serving--where agents, not humans, issue the requests--remains unclear. Agents shift the workload in two ways: they consume many more tokens than humans, so the cluster must provide high throughput (TPS) at low latency; and their requests reuse far more KV\$ than chat. Existing schedulers still trade off load balance against KV\$ reuse: cache-aware schedulers may crowd requests onto the few instances caching the KV\$, leaving the rest idle, while balanced schedulers may lose the opportunity for reuse, which is costly at a high reuse ratio. We thus present two key insights: (1) with a global-tier KV\$ store, p...
| Authors | Jiahao Wang, Kaizhan Lin, Kaixi Zhang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 9 July 2026 |
| Last updated at source | 13 September 2026 |
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