With the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

中文翻译：

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基于遗传算法的超密集蜂窝网络移动边缘计算中的自适应卸载

结合移动边缘计算（MEC）和下一代蜂窝网络，可以通过基站（BS）上的边缘服务器迅速而准确地卸载来自终端设备的计算请求。但是，由于BS的密集异构部署，终端设备可能会被多个BS覆盖，这给卸载决策带来了新挑战，即是否以及在何处卸载计算任务以降低延迟和能源成本。本文提出了超密集蜂窝网络中的多用户到多服务器（MUMS）边缘计算问题。MUMS问题分为两个阶段，分别是服务器选择和卸载决策。在服务器选择阶段，考虑到物理距离和工作量，将移动用户分组到一个BS。分组后 最初的问题分为并行的多用户到一台服务器卸载决策子问题。为了获得这些子问题的快速且接近最优的解决方案，设计了一种基于二进制编码遗传算法的分布式卸载策略来获得自适应卸载决策。给出了遗​​传算法的收敛性分析，并进行了广泛的仿真，结果表明，该策略显着降低了移动设备的平均等待时间和能耗。与最新的卸载研究相比，我们的策略将超密集蜂窝网络的平均延迟降低了56％，总能耗降低了14％。设计了一种基于二进制编码遗传算法的分布式卸载策略，以获得自适应卸载决策。给出了遗​​传算法的收敛性分析，并进行了广泛的仿真，结果表明所提出的策略显着降低了移动设备的平均等待时间和能耗。与最新的卸载研究相比，我们的策略将超密集蜂窝网络的平均延迟降低了56％，总能耗降低了14％。设计了一种基于二进制编码遗传算法的分布式卸载策略，以获得自适应卸载决策。给出了遗​​传算法的收敛性分析，并进行了广泛的仿真，结果表明所提出的策略显着降低了移动设备的平均等待时间和能耗。与最新的卸载研究相比，我们的策略将超密集蜂窝网络的平均延迟降低了56％，总能耗降低了14％。