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Calibrate, Then Route: A Measured Study of Learned Request Routing for Disaggregated LLM Serving
Not provided in the abstract
routingdisaggregated systemsLLM servingperformance optimization
2609.16206
Builder Relevance
1h ago80%
Abstract
This paper studies a calibrated router for disaggregated LLM serving that improves request routing efficiency and goodput.
Reality Card
Core Claim
The calibrated router achieves the highest mean goodput of 0.864 compared to traditional methods like round robin and least loaded routing.
Method / Result
Achieved a mean goodput of 0.864 across three mixed, bursty arrival traces.
Limitations
Simulator derived constants significantly impact performance, leading to concerns about the generalizability of results.
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