The goal of this work is to propose an energy-efficient algorithm for dynamic computation offloading, in a multi-access edge computing scenario, where multiple mobile users compete for a common pool of radio and computational resources. We focus on delay-critical applications, incorporating an upper bound on the probability that the overall time required to send the data and process them exceeds a prescribed value. In a dynamic setting, the above constraint translates into preventing the sum of the communication and computation queues’ lengths from exceeding a given value. Ultra-reliable low latency communications (URLLC) are also taken into account using finite blocklengths and reliability constraints. The proposed algorithm, based on stochastic optimization, strikes an optimal balance between the service delay and the energy spent at the mobile device, while guaranteeing a target out-of-service probability. Starting from a long-term average optimization problem, our algorithm is based on the solution of a convex problem in each time slot, which is provided with a very fast iterative strategy. Finally, we extend the approach to mobile devices having energy harvesting capabilities, typical of Internet of Things scenarios, thus devising an energy efficient dynamic offloading strategy that stabilizes the battery level of each device around a prescribed operating level.

中文翻译：

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通过超可靠和低延迟的通信实现多路访问边缘计算中的动态计算分流

这项工作的目的是为多接入边缘计算场景中的多个移动用户争夺公共的无线电和计算资源池，提出一种用于动态计算分载的节能算法。我们专注于延迟关键型应用程序，并结合了发送数据和处理它们所需的总时间超过规定值的概率的上限。在动态设置中，以上约束转化为防止通信和计算队列长度的总和超过给定值。还使用有限的块长和可靠性约束来考虑超可靠的低延迟通信（URLLC）。所提出的算法基于随机优化，在保证服务中断目标概率的同时，在服务延迟和在移动设备上花费的能量之间达到最佳平衡。从长期平均优化问题开始，我们的算法基于每个时隙凸问题的解决方案，并提供了非常快速的迭代策略。最后，我们将该方法扩展到具有能量收集功能的移动设备（在物联网场景中很典型），从而设计出一种节能高效的动态卸载策略，将每个设备的电池电量稳定在规定的操作水平附近。提供了非常快速的迭代策略。最后，我们将该方法扩展到具有能量收集功能的移动设备（在物联网场景中很典型），从而设计出一种节能高效的动态卸载策略，将每个设备的电池电量稳定在规定的操作水平附近。提供了非常快速的迭代策略。最后，我们将该方法扩展到具有能量收集功能的移动设备（在物联网场景中很典型），从而设计出一种节能高效的动态卸载策略，将每个设备的电池电量稳定在规定的操作水平附近。