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AEDS: An Affinity-Driven Efficient DRL-Based Task Scheduling Framework for Edge Computing
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DOI：10.1109/TMC.2025.3608263.png)
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Abstract
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Edge computing is a promising paradigm that deploys computing resources at the network edge to provide services. Many existing solutions leverage deep reinforcement learning (DRL) to optimize task scheduling, yet they often rely on global scheduling approaches. However, such solutions result in an excessively large decision search space, reducing task scheduling efficiency in complex environments. Additionally, the cold start problem impedes the generation of optimal scheduling strategies. To address these challenges, we propose AEDS, a DRL-based task scheduling framework designed to enhance scheduling efficiency. AEDS optimizes the decision-making process from three aspects: (1) Decision Space Reduction. AEDS incorporates a novel affinity matching mechanism that identifies the most suitable edge cluster based on task characteristics, thereby significantly narrowing the decision search space. (2) Decision Process Optimization. AEDS adopts a hybrid strategy combining offline pre-training and online fine-tuning to address the cold start problem. Offline pre-training with historical task data ensures effective initial scheduling, while online fine-tuning periodically updates the DRL model to enhance long-term adaptability to dynamic system changes. (3) Decision Strategy Calibration. AEDS proposes a task migration solution to adapt to real-time workload variations dynamically. It utilizes triple queues to assess server overload and dynamically calibrates the scheduling strategy through task migration within interconnected clusters. Comprehensive experimental results validate the efficacy of AEDS. Compared with existing frameworks, AEDS reduces task latency by 28.23% and enhances task completion rate by 10.28%. Furthermore, by effectively narrowing the decision scope, AEDS accelerates the decision-making process by a remarkable 88.06%
Keywords:
Internet of Things
edge computing
task scheduling
deep reinforcement learning
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