Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Distributed and Parallel Computing Systems
Abstract
This paper investigates the application of Reinforcement Learning (RL) for dynamic resource scheduling in distributed systems. Traditional resource scheduling methods often rely on static rules or heuristics, which may not adapt effectively to the fluctuating demands and workloads of a distributed environment. This research proposes a novel approach where an RL agent learns to optimize resource allocation based on real-time system state. The agent interacts with the distributed system, observing resource utilization metrics and receiving rewards for efficient scheduling decisions. The core of this work lies in formulating the resource scheduling problem as an RL problem, employing algorithms like Q-learning or Deep Q-Networks (DQN) to train the agent. The ultimate goal is to achieve higher system throughput and improved resource utilization compared to traditional scheduling approaches. This work presents a theoretical framework and explores the potential of RL to dynamically adapt to the complexities of distributed system resource management.
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