Internet of Things (IoT) is considered as the enabling platform for a variety of promising applications, such as smart transportation and smart city, where massive devices are interconnected for data collection and processing. These IoT applications pose a high demand on storage and computing capacity, while the IoT devices are usually resource constrained. As a potential solution, mobile edge computing (MEC) deploys cloud resources in the proximity of IoT devices so that their requests can be better served locally. In this work, we investigate computation offloading in a dynamic MEC system with multiple edge servers, where computational tasks with various requirements are dynamically generated by IoT devices and offloaded to MEC servers in a time-varying operating environment (e.g., channel condition changes over time). The objective of this work is to maximize the completed tasks before their respective deadlines and minimize energy consumption. To this end, we propose an end-to-end Deep Reinforcement Learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. The simulation results are provided to demonstrate that the proposed approach outperforms the existing methods.

中文翻译：

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使用深度强化学习的移动边缘计算中的延迟感知和节能计算卸载


物联网 (IoT) 被认为是各种有前途的应用的支持平台，例如智能交通和智能城市，其中大量设备互连以进行数据收集和处理。这些物联网应用对存储和计算能力提出了很高的要求，而物联网设备通常资源有限。作为一种潜在的解决方案，移动边缘计算 (MEC) 在物联网设备附近部署云资源，以便可以在本地更好地满足其请求。在这项工作中，我们研究了具有多个边缘服务器的动态MEC系统中的计算卸载，其中具有各种要求的计算任务由物联网设备动态生成，并在时变操作环境中卸载到MEC服务器（例如，信道条件随时间变化） ）。这项工作的目标是在各自的截止日期之前最大限度地完成任务并最大限度地减少能源消耗。为此，我们提出了一种端到端深度强化学习（DRL）方法来选择最佳边缘服务器进行卸载并分配最佳计算资源，从而最大化预期的长期效用。仿真结果证明所提出的方法优于现有方法。