A resource-fair batching policy (ISJL) that keeps co-batched LLM requests within a token-progress window is proved 3/4-competitive in an offline model and empirically outperforms FCFS, SJF, and LJF on throughput and latency.
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Efficiency and Cost Alignment in Batched LLM Serving via Resource-Fair Scheduling
A resource-fair batching policy (ISJL) that keeps co-batched LLM requests within a token-progress window is proved 3/4-competitive in an offline model and empirically outperforms FCFS, SJF, and LJF on throughput and latency.