Figure - available from: The Journal of Supercomputing
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The obtained results of applying various weight sets on reward function terms including the execution time, power consumption, reliability and temperature
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In this paper, a Q-learning-based task scheduling approach for mixed-critical application on heterogeneous multi-cores (QSMix) to optimize their main design challenges is proposed. This approach employs reinforcement learning capabilities to optimize execution time, power consumption, reliability and temperature of the heterogeneous multi-cores dur...
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In the field of safe reinforcement learning (RL), finding a balance between satisfying safety constraints and optimizing reward performance presents a significant challenge. A key obstacle in this endeavor is the estimation of safety constraints, which is typically more difficult than estimating a reward metric due to the sparse nature of the const...
Citations
... Among them, Q-learning, as a reinforcement learning method, has attracted much attention because it can learn adaptive strategies through experience in dynamic environments. Q-learning can maximize resource utilization when computing resources are limited, thereby optimizing system performance and improving overall efficiency [9]. ...
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.
