CacheRoute: Planned Prefix-Affinity Routing for Large-Scale LLM Serving
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Computer Science > Distributed, Parallel, and Cluster Computing
Title:CacheRoute: Planned Prefix-Affinity Routing for Large-Scale LLM Serving
Abstract:Prefix caching avoids prefill only when a repeated request returns to a server that still holds the prefix KV. Cache-blind balancing disperses that reuse; fixed affinity preserves it but can overload a server. CacheRoute resolves this tradeoff with a periodic routing plan. It admits high-rate keys to a stable warm set and places their assignments by expected load. Hot keys may use more than one destination, although every key in our primary semi-synthetic aggregate uses exactly one. On Llama-3.3-70B in fp8 across 60 H100 GPUs, CacheRoute sustains 176+/-11 QPS at a 3.5-s p99 SLO, 2.3x the strongest of five baselines. Served KV-cache hit rate rises from 64.1+/-1.3% under cache-blind balancing to 93.2+/-0.5%. A second semi-synthetic aggregate and controlled 8B and burst experiments separate the effects of affinity and placement. Two 32B workloads provide the counterexamples: when affinity recovers too little KV work, its residual load skew reduces or erases the improvement. We therefore recommend gating any deployment with a shadow replay rather than enabling affinity from workload statistics alone.
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.19677 [cs.DC] |
| (or arXiv:2608.19677v1 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2608.19677
arXiv-issued DOI via DataCite (pending registration)
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