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<p>In Mobile Edge Computing (MEC) environments, offloading computational tasks to edge devices can reduce delay and energy consumption, thereby enhancing the user experience. In practical application scenarios, some complex computational tasks often exhibit temporal dependencies, meaning that the execution of one task relies on the results of previously executed tasks. When offloading such tasks, it is necessary to consider both the execution order and the data dependencies between tasks, which increases the complexity of modeling and solving the task offloading problem. Therefore, this paper constructs a task offloading model with temporal dependencies by comprehensively considering the communication cost between tasks, offloading delay, and energy consumption. Additionally, a Deep Q-Network (DQN)-based algorithm is proposed to address the temporal dependency task offloading model. The state space, action space, and optimized reward function tailored to the temporal dependency task offloading scenario are designed to accelerate the convergence of the algorithm. Finally, experimental results in real datasets demonstrate that the method proposed in this paper can achieve more consumption-effective offloading decisions while maintaining strong performance in various temporal dependency task offloading scenarios.</p>

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Temporal dependency task offloading via deep reinforcement learning for mobile edge computing

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Xiuguo Zhang,
  • Chenqian Fang,
  • Zexin Bai,
  • Lincai Zhang,
  • Peipeng Wang,
  • Zhiying Cao

摘要

In Mobile Edge Computing (MEC) environments, offloading computational tasks to edge devices can reduce delay and energy consumption, thereby enhancing the user experience. In practical application scenarios, some complex computational tasks often exhibit temporal dependencies, meaning that the execution of one task relies on the results of previously executed tasks. When offloading such tasks, it is necessary to consider both the execution order and the data dependencies between tasks, which increases the complexity of modeling and solving the task offloading problem. Therefore, this paper constructs a task offloading model with temporal dependencies by comprehensively considering the communication cost between tasks, offloading delay, and energy consumption. Additionally, a Deep Q-Network (DQN)-based algorithm is proposed to address the temporal dependency task offloading model. The state space, action space, and optimized reward function tailored to the temporal dependency task offloading scenario are designed to accelerate the convergence of the algorithm. Finally, experimental results in real datasets demonstrate that the method proposed in this paper can achieve more consumption-effective offloading decisions while maintaining strong performance in various temporal dependency task offloading scenarios.