MARS: A Monte Carlo Tree Search-based Adaptive and Responsive Scheduler

2026-08-06

AI Summary

This work addresses the limitations of traditional high-performance computing (HPC) schedulers, which struggle to dynamically adapt to changing optimization objectives due to reliance on static heuristics or the need for model retraining. The authors propose MARS, the first training-free scheduling framework that integrates Monte Carlo Tree Search (MCTS) into HPC scheduling. MARS combines a lightweight discrete-event simulator with a configurable reward function to prospectively explore the consequences of scheduling decisions within each scheduling cycle. It enables dynamic adjustment of optimization goals without requiring historical data or model retraining and proactively plans resource draining and backfilling ahead of maintenance windows. Experiments on the Theta and Polaris systems demonstrate that MARS-CW reduces tail job waiting time by 64% and 43%, respectively, while MARS-CU significantly improves system utilization in the 48 hours preceding scheduled maintenance.

Abstract

Modern High Performance Computing systems depend on static heuristics and manual administration for job scheduling and reservation management. Deep Reinforcement Learning (DRL) has shown promising scheduling performance but requires historical training data and fixes the optimization goal at training time, forcing operators to retrain whenever priorities shift. We introduce MARS (Monte Carlo Tree Search-based Adaptive and Responsive Scheduler), a training-free HPC scheduler whose optimization goal is configurable through a reward function rather than baked into a learned model. MARS uses a lightweight discrete-event simulator to explore the future consequences of scheduling decisions within a strict time budget, adapting to the configured reward at each scheduling cycle. We evaluate MARS on year-long production workloads from two systems at Argonne Leadership Computing Facility -- 4,360-node Theta and 560-node Polaris---under two reward functions: wait-time minimization (MARS-CW) and utilization maximization (MARS-CU). Unlike DRL and heuristics, which only react to the current queue or wait for backfill to find holes, MARS exploits look-ahead to proactively drain the system and plan around future reservations, packing the system to avoid the fragmentation and utilization drop that typically precede reservation windows. MARS-CW reduces tail wait time by 64% on Theta and 43% on Polaris over the production WFP heuristic, while MARS-CU recovers utilization in the 48 hours leading into maintenance, demonstrating that MARS can target either objective via reward reconfiguration.

Problem

• High Performance Computing
• Job Scheduling
• Adaptive Optimization
• Dynamic Priorities
• Resource Utilization

Innovation

• Monte Carlo Tree Search
• adaptive scheduling
• reward-configurable optimization
• discrete-event simulation
• HPC job scheduling