Huang Peng, Deng Minjiang, Kang Zhiliang, Liu Qinshan, Xu Lijia
College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an 625000, China.
Entropy (Basel). 2021 Aug 31;23(9):1146. doi: 10.3390/e23091146.
Mobile edge computing (MEC) focuses on transferring computing resources close to the user's device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.
移动边缘计算（MEC）专注于将计算资源转移到靠近用户设备的位置，并为移动设备提供高性能和低延迟服务。它是处理计算密集型和延迟敏感型任务的有效方法。鉴于城市地区移动设备存在大量未充分利用的计算资源，利用这些未充分利用的资源可带来巨大的机遇和价值。考虑到MEC系统中用户设备的时空动态、丰富计算资源的不确定性以及网络信道状态，具有空闲计算资源的移动设备中的计算资源分配将影响任务请求的响应时间。为解决这些问题，本文考虑移动设备在卸载计算请求时可向相邻物联网设备学习的情况。在此基础上，设计了一种新颖的任务卸载自适应学习算法（SAda），以最小化MEC系统中的平均卸载延迟。SAda采用分布式工作模式，具有感知功能以适应现实中的动态环境；它不需要频繁访问设备信息。大量仿真表明，与现有的上限算法相比，SAda实现了更好的延迟性能和较低的学习误差。
