arXiv cs.AI· Yuyu Zhang·· 6 天前AI 评分20
等预算下 MCTS 与随机重启列表调度在 DAG 工作流上的对照计算研究
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study
AI 导读
一项预注册的等预算对照研究显示，在 2048 次候选调度评估的预算下，MCTS 调度方法比无搜索树的随机重启列表调度差 2.88%（95% CI [-3.62%,-2.15%]）。
正文 · 原文
Abstract:Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that could refute it - giving a much simpler method the same search effort. We run that comparison under a controlled protocol. All arms receive an identical budget - the same number of candidate schedules built and evaluated: an MCTS scheduling method, preregistered as the main arm; a randomized-restart list scheduler with no search tree as the control; and a textbook-UCT MCTS arm as a registered follow-up on the same domain. The analysis plan was locked before data collection; an independent checker confirmed all 768 confirmation-stage schedules valid with correct finish times. At the main budget of 2048 evaluated schedules, MCTS is 2.88% worse than the restarts (95% CI [-3.62%,-2.15%]; the 72 of 96 held-out instances with positive baseline-relative headroom). The deficit is not budget starvation: on the separate exploration pool restarts self-improve by 1.85% where MCTS gains 0.24%, and 700 of 720 of MCTS's first results are merely the warm start it was handed. The studied selection rule locks deterministically onto the first-expanded child, keeping its trials correlated while restarts draw gains from the tails; the causal link to makespan remains an inference. A structural map locates where the tree stops paying: restarts win on sparse irregular workflows, no difference is detectable on regular layered ones, and dense ones leave no room to improve. Swapping the rule for textbook UCT removes most of the deficit (-0.26%, 95% CI [-0.71%,+0.23%]) without reversing its sign. Results are confined to a controlled synthetic domain (N in {8,16,32} tasks, 2-8 processors, budgets up to 2048); we make no claim about larger budgets.
| Comments: | 19 pages, 4 figures. Preregistered negative result with an open evaluation protocol; companion artifacts archived at https://doi.org/10.5281/zenodo.22929494 and https://doi.org/10.5281/zenodo.22930513 |
| Subjects: | Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.12542 [cs.AI] |
| (or arXiv:2610.12542v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12542 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuyu Zhang [view email]
[v1]
Thu, 8 Oct 2026 16:01:53 UTC (241 KB)
来源：arXiv cs.AI · arxiv.org