Mil: Cost-guided Minimum Makespan Scheduling for Applications of Multiple LLMs
Summary: Mil formulates offline multi-LLM inference as an NP-hard minimum-makespan problem coupling GPU allocation, parallelism, and orchestration under relaxed precedences. Its cost-guided rate estimation, theoretically grounded greedy scheduling, and runtime adjustment deliver up to 3.4× speedups. (summarized by gpt-5.6-luna on Aug 17 2026)
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Authors
- 1. Jingzhi Fang (Hong Kong University of Science and Technology)
- 2. Yanyan Shen (Shanghai Jiao Tong University)
- 3. Yue Wang (Shenzhen University)
- 4. Lei Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@article{fang_vldb26,
title = {{Mil: Cost-guided Minimum Makespan Scheduling for Applications of Multiple LLMs}},
author = {Fang, Jingzhi and Shen, Yanyan and Wang, Yue and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {9},
pages = {1893--1906},
doi = {10.14778/3819518.3819522},
url = {https://doi.org/10.14778/3819518.3819522},
year = {2026}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 329 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00020858443 |
| 3,973 | RetClean: Retrieval-Based Data Cleaning Using LLMs and Data Lakes | 2024 | VLDB | 6.8876964e-05 |
| 7,069 | Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads | 2024 | VLDB | 5.6083188e-05 |
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