A fast rate of progress has allowed the proliferation of smartphones and eased their extensive presence in people's daily life. However, low processing speed and limited battery capacity have hindered improvements in the smartphone's computational capabilities. Offloading computational tasks to the cloud could solve this problem by enabling users to access these services over the Internet. Edge cloud computing has been recognized as an emerging field within the cloud computing paradigm, where computation servers are situated at the edge of the Internet to reduce network delay and traffic. Nevertheless, offloading tasks to the cloud is not always beneficial due to variable network conditions and increased processing costs. In this paper, a deep reinforcement learning-based offloading framework has been presented that provides smartphones with the ability to make decisions for local processing in the smartphone or to offload processing tasks to the cloud (edge and/or core). Thus, a smartphone can minimize the combination of the processing time, energy consumption, and monetary cost and maximize the accuracy of face recognition as well. Simulation results under synthetic scenarios show that the proposed offloading framework can effectively adapt to the dynamic cloud computing and networking environment.

中文翻译：

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边缘云和核心云计算服务卸载框架：​​以人脸识别为例

快速的进步使得智能手机得以普及，并简化了它们在人们日常生活中的广泛存在。然而，低处理速度和有限的电池容量阻碍了智能手机计算能力的提高。通过使用户能够通过 Internet 访问这些服务，将计算任务卸载到云可以解决这个问题。边缘云计算被认为是云计算范式中的一个新兴领域，其中计算服务器位于互联网的边缘以减少网络延迟和流量。然而，由于网络条件的变化和处理成本的增加，将任务卸载到云并不总是有益的。在本文中，已经提出了基于深度强化学习的卸载框架，该框架为智能手机提供了为智能手机中的本地处理做出决策或将处理任务卸载到云（边缘和/或核心）的能力。因此，智能手机可以最大限度地减少处理时间、能源消耗和货币成本的组合，并最大限度地提高人脸识别的准确性。综合场景下的仿真结果表明，所提出的卸载框架可以有效地适应动态的云计算和网络环境。和货币成本，并最大限度地提高人脸识别的准确性。综合场景下的仿真结果表明，所提出的卸载框架可以有效地适应动态的云计算和网络环境。和货币成本，并最大限度地提高人脸识别的准确性。综合场景下的仿真结果表明，所提出的卸载框架可以有效地适应动态的云计算和网络环境。