In this article, we investigate code-oriented partitioning computation offloading strategy for multiple user equipments (UEs) and multiple mobile edge computing servers with limited resources (i.e., limited computing power and waiting task queues with finite capacity). This article aims to develop an offloading strategy to decide the execution location, CPU frequency, and transmission power for UE while minimizing the execution overhead (i.e., a weighted sum of energy consumption and computational time) of UE's applications, which is an NP-hard problem. To achieve the objective, first, we transform the problem into a convex optimization problem and find the optimal solution. Second, we propose a decentralized computation offloading strategy (DCOS) algorithm for UE, and define a dictionary data structure for recording the strategy of the UE to reduce the algorithm complexity. Finally, the effectiveness of DCOS, and the impact of various key parameters on the strategy and overhead are demonstrated by simulation experiments.

中文翻译：

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多用户和多移动边缘计算服务器的面向代码的分区计算卸载策略

在本文中，我们研究了具有有限资源（即，有限的计算能力和有限容量的等待任务队列）的多个用户设备（UE）和多个移动边缘计算服务器的面向代码的分区计算卸载策略。本文旨在开发一种卸载策略，以决定UE的执行位置，CPU频率和传输功率，同时最大程度地减少UE应用程序的执行开销（即能耗和计算时间的加权总和），这是一个NP难题。问题。为了达到目的，首先，我们将问题转化为凸优化问题，并找到最优解。其次，我们为UE提出了一种分散式计算卸载策略（DCOS）算法，定义用于记录UE策略的字典数据结构，以降低算法复杂度。最后，通过仿真实验证明了DCOS的有效性以及各种关键参数对策略和开销的影响。