Tail-Aware Scheduling Reduces Workflow Latency
Researchers at Agentic have developed a new method for scheduling LLM workflows that takes into account the readiness of individual model turns, rather than releasing them immediately upon completion. This approach aims to reduce tail latency by managing unfinished work more effectively. The method uses a mean-Conditional Value-at-Risk (CVaR) objective to anticipate and mitigate potential delays…
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Key points
- New tail-aware scheduling method developed by Agentic
- Reduces workflow latency and tail risk through better turn management
- Significantly reduces P95 workflow flow time compared to eager release policy
Read the original at arXiv cs.AI · by Bochao Feng, Jianjiang Li, Haojie Wang, Lin Qiao, Yinghui Li, Yukun Yan, Jidong Zhai primary source Open source ↗
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