iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems

2026-01-30

arXiv.org

AI Summary

为解决大规模资源投资问题中的调度难题，提出iScheduler框架，利用强化学习驱动迭代优化，加速求解并支持快速重配置。

Abstract

Scheduling precedence-constrained tasks under shared renewable resources is central to modern computing platforms. The Resource Investment Problem (RIP) models this setting by minimizing the cost of provisioned renewable resources under precedence and timing constraints. Exact mixed-integer programming and constraint programming become impractically slow on large instances, and dynamic updates require schedule revisions under tight latency budgets. We present iScheduler, a reinforcement-learning-driven iterative scheduling framework that formulates RIP solving as a Markov decision process over decomposed subproblems and constructs schedules through sequential process selection. The framework accelerates optimization and supports reconfiguration by reusing unchanged process schedules and rescheduling only affected processes. We also release L-RIPLIB, an industrial-scale benchmark derived from cloud-platform workloads with 1,000 instances of 2,500-10,000 tasks. Experiments show that iScheduler attains competitive resource costs while reducing time to feasibility by up to 43$\times$ against strong commercial baselines.

Problem

• Resource Investment Problem
• scheduling
• renewable resources
• precedence constraints
• latency budgets

Innovation

• Reinforcement Learning
• Markov Decision Process
• Iterative Scheduling
• Large-Scale Resource Investment Problem
• Dynamic Updates