Scheduling for Green Computility via LLM-Guided Deep Reinforcement Learning

Abstract

Green Computility scheduling in heterogeneous clusters requires balancing execution efficiency, deadline satisfaction, resource utilization, carbon emissions, and renewable energy usage under dynamic workload and energy conditions. Existing heuristic schedulers are efficient but often rely on fixed hand crafted rules, whereas reinforcement learning (RL) based schedulers may explore inefficiently in large and structured action spaces when no prior guidance is available. To address these challenges, we propose Green-LLM-DQN, a large language model (LLM) guided dueling double deep Q-network (D3QN) framework for trace-driven Green Computility scheduling. The framework uses an LLM to generate a structured and interpretable heuristic prior from the scheduling context, and incorporates this prior into a D3QN backbone for action scoring and selection. This design improves early stage exploration while preserving the adaptability of RL. Experiments on trace driven workloads show that Green-LLM-DQN achieves a competitive overall trade-off among return, carbon emissions, and green energy utilization than classical scheduling heuristics and learning based baselines.