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bursty-workloads

topic1 events
papersTODAY 04:00 UTC

MAPS: Memory-Aware Predictive Scheduling for LLM Serving

Researchers propose MAPS, a scheduling framework designed to handle bursty large language model workloads on cloud infrastructure. The work targets memory-bound decode instances in prefill-decode disaggregated serving setups, where memory pressure limits throughput. It aims to improve scheduling decisions by predicting memory needs ahead of time.

llm-serving/scheduling/inference/arxiv