文献类型：期刊文献

英文题名：Adaptive Task Scheduling Under Dynamic Edge System Loads: A Deep Reinforcement Learning Approach

作者：Xu, Jin[1];Yu, Huiqun[1];Fan, Guisheng[1];Zhang, Hengrun[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China

年份：2025

卷号：37

期号：27-28

外文期刊名：CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

收录：;EI(收录号：20254519469237);WOS:【SCI-EXPANDED(收录号:WOS:001626088000013)】；

基金：This work was supported by The Natural Science Foundation of Shanghai (Grant No. 21ZR1416300), The Capacity Building Project of Local Universities Science and Technology Commission of Shanghai Municipality (Grant No. 22010504100), The Research Programme of National Engineering Laboratory for Big Data Distribution and Exchange Technologies, and the Shanghai Municipal Special Fund for Promoting High Quality Development (Grant No. 2021-GYHLW-01007).

语种：英文

外文关键词：edge computing; sac-based adaptive algorithm; task scheduling

摘要：Edge computing systems are in great need of task scheduling due to resource constraints. However, existing scheduling algorithms typically optimize single objectives and lack adaptability to varying system conditions, failing to balance response time minimization during low workloads with queue balance maintenance under high workloads. This paper proposes an adaptive task scheduling algorithm based on Soft Actor-Critic (SAC) with a novel workload-aware reward mechanism, which automatically transitions between response time optimization and queue balance prioritization according to system load conditions. The whole scheduling problem is modeled as a Markov Decision Process (MDP), and a sliding window-based performance evaluation framework is introduced to provide robust system assessment. Extensive experiments across multiple scenarios demonstrate that our method consistently achieves optimal response time across varying workload conditions, significantly outperforming traditional scheduling algorithms, while maintaining effective queue balance comparable to load-based approaches under high workload scenarios.