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

ABSTRACT 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.