Summary of Don’t Stop Me Now: Embedding Based Scheduling For Llms, by Rana Shahout et al.
Don’t Stop Me Now: Embedding Based Scheduling for LLMs
by Rana Shahout, Eran Malach, Chunwei Liu, Weifan Jiang, Minlan Yu, Michael Mitzenmacher
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TRAIL method enables Large Language Model (LLM) applications to efficiently schedule requests by predicting output lengths using the target LLM itself. This approach addresses challenges in size-based scheduling, including accurate request size estimation and memory overhead from preemption. The paper introduces a prediction-based Shortest Remaining Process Time (SRPT) variant that limits preemption when memory consumption is low and restricts it as requests near completion to optimize resource utilization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TRAIL helps LLM systems manage interactive applications by predicting output lengths using the target model itself. This improves request scheduling, reducing average completion time and enhancing user engagement. The method also reduces memory overhead from preemption, making it a more efficient way to schedule requests in LLM systems. |
Keywords
» Artificial intelligence » Large language model
