iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems
This addresses the need for fast, scalable scheduling in cloud platforms, offering an incremental improvement over existing methods.
The paper tackles the Resource Investment Problem (RIP) for scheduling tasks under resource constraints by introducing iScheduler, a reinforcement learning framework that reduces time to feasibility by up to 43x compared to commercial baselines while maintaining competitive resource costs.
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.