Pruning-based Deep Reinforcement Learning for Task Offloading in End-Edge-Cloud Collaborative Mobile Edge Computing
DOI:
https://doi.org/10.54097/291dyi3rKeywords:
Mobile edge computing, Task offloading, Deep reinforcement learning, Multi-objective optimizationAbstract
The task offloading of Mobile Edge Computing (MEC) brings infinite possibilities to compute-intensive and latency-sensitive mobile applications. However, the dynamic nature of MEC systems and complex dependencies between computational tasks pose significant challenges to offloading decisions. In this paper, we address the task offloading problem of end-edge-cloud collaborative computing in MEC with task dependencies. Initially, we model inter-task dependencies using Directed Acyclic Graphs (DAG) and propose a task priority queue model to transform the DAG task model into a sequential queue model for easier task scheduling. Subsequently, we formulate a resource-constrained minimization problem for execution delay and energy consumption optimization. To tackle this problem, we introduce a Pruning-based Deep Reinforcement Learning algorithm (PR-DRL) to learn the intricate dependencies between MEC systems and subtasks for optimal offloading decisions. Specifically, PR-DRL incorporates a pruning function that enables the agent to focus on high-probability actions during training while filtering out low-probability actions, thereby achieving rapid algorithm convergence. Simulation results demonstrate that the proposed PR-DRL method outperforms traditional Deep Q Network methods both in terms of convergence speed and offloading performance, surpassing six other baseline task offloading methods.
References
Nadir, Zinelaabidine, et al. Immersive services over 5G and beyond mobile systems. IEEE Network 35.6 (2021): 299-306.
Antonopoulos, Nick, and Lee Gillam. Cloud computing. Vol. 51. No. 7. London: Springer, 2010.
Mao, Yuyi, et al. A survey on mobile edge computing: The communication perspective. IEEE communications surveys & tutorials 19.4 (2017): 2322-2358.
Carvalho, Gonçalo, et al. Edge computing: current trends, research challenges and future directions. Computing 103.5 (2021): 993-1023.
Hu, Yun Chao, et al. Mobile edge computing—A key technology towards 5G. ETSI white paper 11.11 (2015): 1-16.
Qin, Meng, et al. Service-oriented energy-latency tradeoff for IoT task partial offloading in MEC-enhanced multi-RAT networks. IEEE Internet of Things Journal 8.3 (2020): 1896-1907.
Yu, Wenmeng, and Hua Xu. Co-attentive multi-task convolutional neural network for facial expression recognition. Pattern Recognition 123 (2022): 108401.
Schulman, John, et al. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017).
Tang, Cheng-Yen, et al. Implementing action mask in proximal policy optimization (PPO) algorithm.ICT Express 6.3 (2020): 200-203.
Schulman, John, et al. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 (2015).
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