Digital Twin-Driven Intelligent Task Offloading for Collaborative Mobile Edge Computing
Digital Twin-Driven Intelligent Task Offloading for Collaborative Mobile Edge Computing
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DOI：
10.1109/jsac.2023.3310058
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发表时间：
2023-10
影响因子：
16.4
通讯作者：
Yongchao Zhang;Jia Hu;Geyong Min
中科院分区：
文献类型：
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作者：
Yongchao Zhang;Jia Hu;Geyong Min
协作移动的边缘计算（MEC）是一种新的范例，其允许分布式MEC服务器之间的协作对等卸载以平衡其计算工作负载。然而，高度动态的工作负载和无线网络条件对协作MEC中实现高效的任务卸载提出了巨大的挑战。为了应对这一挑战，数字孪生（DT）已经成为一种有前途的解决方案，它通过构建物理MEC的高保真虚拟镜像来模拟其行为并帮助做出最佳运营决策。本文提出了一种DT驱动的协同MEC智能任务卸载框架，利用DT将协同MEC系统映射到虚拟空间，优化任务卸载决策。我们将任务卸载过程建模为马尔可夫决策过程（MDP），目标是最大化MEC系统提供计算服务的总收入，然后开发基于深度强化学习（DRL）的智能任务卸载方案（INTO），以联合优化对等卸载和资源分配决策。提出了一种有效的动作细化方法，以确保DRL代理选择的动作是可行的。实验结果表明，我们提出的方法可以有效地适应根据动态环境的任务卸载决策，并显着提高MEC系统的收入通过广泛的比较与三个国家的最先进的算法。
Collaborative mobile edge computing (MEC) is a new paradigm that allows cooperative peer offloading among distributed MEC servers to balance their computing workloads. However, the highly dynamic workloads and wireless network conditions pose great challenges to achieving efficient task offloading in collaborative MEC. To address this challenge, digital twin (DT) has emerged as one promising solution by building a high-fidelity virtual mirror of the physical MEC to simulate its behaviors and help make optimal operational decisions. In this paper, we propose a DT-driven intelligent task offloading framework for collaborative MEC, where DT is employed to map the collaborative MEC system into a virtual space and optimize the task offloading decisions. We model the task offloading process as a Markov decision process (MDP) with the objective of maximizing the MEC system’s total income from providing computing services, and then develop a deep reinforcement learning (DRL)-based intelligent task offloading scheme (INTO) to jointly optimize the peer offloading and resource allocation decisions. An efficient action refinement method is proposed to ensure that the action selected by the DRL agent is feasible. Experimental results show that our proposed approach can effectively adapt the task offloading decisions according to the dynamic environment, and significantly improve the MEC system’s income through extensive comparison with three state-of-the-art algorithms.