Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading
Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading
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DOI：
10.1109/mwc.001.1900232
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发表时间：
2020-02-01
影响因子：
12.9
通讯作者：
Zhang, Junshan
中科院分区：
文献类型：
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作者：
Yu, Shuai;Chen, Xu;Zhang, Junshan
在这项工作中，我们为MEC网络提出了一种新的深度模仿学习（DIL）驱动的边缘云计算卸载框架。该框架的一个关键目标是通过最优行为克隆使时变网络环境下的卸载成本最小化。具体来说，我们首先详细介绍了MEC的计算卸载模型。然后对移动设备进行细粒度的卸载决策，并将该问题表述为考虑本地执行成本和远程网络资源使用的多标签分类问题。为了最小化卸载成本，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，该方法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MEC多个研究领域的方向和优势，包括边缘数据分析、动态资源分配、安全性和隐私性。
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.