Efficient Serving of LLM Applications with Probabilistic Demand Modeling
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912436878049280 |
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| author | Liu, Yifei Gan, Zuo Gan, Zhenghao Wang, Weiye Chen, Chen Shan, Yizhou Chen, Xusheng Han, Zhenhua Zhu, Yifei Sun, Shixuan Guo, Minyi |
| author_facet | Liu, Yifei Gan, Zuo Gan, Zhenghao Wang, Weiye Chen, Chen Shan, Yizhou Chen, Xusheng Han, Zhenhua Zhu, Yifei Sun, Shixuan Guo, Minyi |
| contents | Applications based on Large Language Models (LLMs) contains a series of tasks to address real-world problems with boosted capability, which have dynamic demand volumes on diverse backends. Existing serving systems treat the resource demands of LLM applications as a blackbox, compromising end-to-end efficiency due to improper queuing order and backend warm up latency. We find that the resource demands of LLM applications can be modeled in a general and accurate manner with Probabilistic Demand Graph (PDGraph). We then propose Hermes, which leverages PDGraph for efficient serving of LLM applications. Confronting probabilistic demand description, Hermes applies the Gittins policy to determine the scheduling order that can minimize the average application completion time. It also uses the PDGraph model to help prewarm cold backends at proper moments. Experiments with diverse LLM applications confirm that Hermes can effectively improve the application serving efficiency, reducing the average completion time by over 70% and the P95 completion time by over 80%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Serving of LLM Applications with Probabilistic Demand Modeling Liu, Yifei Gan, Zuo Gan, Zhenghao Wang, Weiye Chen, Chen Shan, Yizhou Chen, Xusheng Han, Zhenhua Zhu, Yifei Sun, Shixuan Guo, Minyi Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning Applications based on Large Language Models (LLMs) contains a series of tasks to address real-world problems with boosted capability, which have dynamic demand volumes on diverse backends. Existing serving systems treat the resource demands of LLM applications as a blackbox, compromising end-to-end efficiency due to improper queuing order and backend warm up latency. We find that the resource demands of LLM applications can be modeled in a general and accurate manner with Probabilistic Demand Graph (PDGraph). We then propose Hermes, which leverages PDGraph for efficient serving of LLM applications. Confronting probabilistic demand description, Hermes applies the Gittins policy to determine the scheduling order that can minimize the average application completion time. It also uses the PDGraph model to help prewarm cold backends at proper moments. Experiments with diverse LLM applications confirm that Hermes can effectively improve the application serving efficiency, reducing the average completion time by over 70% and the P95 completion time by over 80%. |
| title | Efficient Serving of LLM Applications with Probabilistic Demand Modeling |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.14851 |