In this work, we propose a new deep imitation learning (DIL)-driven edge-cloud computation offloading framework for MEC networks. A key objective for the framework is to minimize the offloading cost in time-varying network environments through optimal behavioral cloning. Specifically, we first introduce our computation offloading model for MEC in detail. Then we make fine-grained offloading decisions for a mobile device, and the problem is formulated as a multi-label classification problem, with local execution cost and remote network resource usage consideration. To minimize the offloading cost, we train our decision making engine by leveraging the deep imitation learning method, and further evaluate its performance through an extensive numerical study. Simulation results show that our proposal outperforms other benchmark policies in offloading accuracy and offloading cost reduction. At last, we discuss the directions and advantages of applying deep learning methods to multiple MEC research areas, including edge data analytics, dynamic resource allocation, security, and privacy, respectively.

中文翻译：

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Intelligent Edge：利用深度模仿学习进行移动边缘计算分流

在这项工作中，我们提出了一种新的深度模仿学习（DIL）驱动的MEC网络边缘云计算卸载框架。该框架的主要目标是通过最佳行为克隆在时变网络环境中最大程度地降低卸载成本。具体来说，我们首先详细介绍MEC的计算分流模型。然后，我们为移动设备做出细粒度的卸载决策，并将该问题表述为具有本地执行成本和远程网络资源使用考虑因素的多标签分类问题。为了最大程度地降低卸载成本，我们利用深度模仿学习方法训练决策引擎，并通过广泛的数值研究进一步评估其性能。仿真结果表明，我们的建议在卸载精度和降低卸载成本方面优于其他基准策略。最后，我们讨论了将深度学习方法应用于多个MEC研究领域的方向和优势，分别包括边缘数据分析，动态资源分配，安全性和隐私性。