Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments
It addresses the need for adaptive workload management in complex systems like edge and cloud computing, offering a scalable solution to improve performance and reduce costs, though it is incremental as it applies an existing reinforcement learning method to a known bottleneck.
This study tackled the problem of inefficient resource allocation in dynamic computing environments by developing a Q-learning-based scheduling algorithm, which outperformed traditional and dynamic resource allocation methods in task completion time and resource utilization.
This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, task complexity, and dynamic workloads, traditional static scheduling methods such as Round-Robin and Priority Scheduling fail to meet the demands of efficient resource allocation and real-time adaptability. By contrast, Q-learning, a reinforcement learning algorithm, continuously learns from system state changes, enabling dynamic scheduling and resource optimization. Through extensive experiments, the superiority of the proposed approach is demonstrated in both task completion time and resource utilization, outperforming traditional and dynamic resource allocation (DRA) algorithms. These findings are critical as they highlight the potential of intelligent scheduling algorithms based on reinforcement learning to address the growing complexity and unpredictability of computing environments. This research provides a foundation for the integration of AI-driven adaptive scheduling in future large-scale systems, offering a scalable, intelligent solution to enhance system performance, reduce operating costs, and support sustainable energy consumption. The broad applicability of this approach makes it a promising candidate for next-generation computing frameworks, such as edge computing, cloud computing, and the Internet of Things.