.png){kind=icon}
Return
Temporal dependency task offloading via deep reinforcement learning for mobile edge computing
.png){kind=icon}
PRE
AI
DOI：10.1007/s12083-025-02101-w.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Mobile edge computing
Task offloading
Temporal dependency
Deep reinforcement learning
Journal
IF:
2.6
Papers:
2.2K
Citations:
2.9K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSave
ShareSaveShareSaveShareSaveShareSave
View more