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cloud.google.com·7h ago
AI Research
arXiv CS.AI·19h ago·5 min read
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study
TL;DR
A controlled study compares Equal-Budget Monte Carlo Tree Search (MCTS) with Randomized-Restart List Scheduling for scheduling directed-acyclic-graph (DAG) workflows. Results show MCTS is 2.88% less effective than the simpler method under identical computational budgets.
✦ Why It Matters
Consider using Randomized-Restart List Scheduling for DAG workflows to optimize scheduling performance.
Key Takeaways
- Evaluate scheduling methods under equal computational budgets.
- Consider simpler algorithms for DAG workflows to enhance performance.
- Explore the impact of selection rules on scheduling efficiency.
- Utilize findings to inform scheduling strategy decisions.
How It Works
MCTS uses a tree structure to explore potential schedules, selecting the first expanded child deterministically. In contrast, the Randomized-Restart List Scheduler generates diverse schedules through random restarts, allowing it to explore a wider range of solutions.