Multi-Access Edge Computing (MEC) emerged as a viable computing allocation method that facilitates offloading tasks to edge servers for efficient processing. The integration of MEC with 5G, referred to as 5G-MEC, provides real-time processing and data-driven decision-making in close proximity to the user. The 5G-MEC has gained significant recognition in task offloading as an essential tool for applications that require low delay. Nevertheless, few studies consider the dropped task ratio metric. Disregarding this metric might possibly undermine system efficiency. In this paper, the dropped task ratio and delay has been minimized in a realistic 5G-MEC task offloading scenario implemented in NS3. We utilize Mixed Integer Linear Programming (MILP) and Genetic Algorithm (GA) to optimize delay and dropped task ratio. We examined the effect of the number of tasks and users on the dropped task ratio and delay. Compared to two traditional offloading schemes, First Come First Serve (FCFS) and Shortest Task First (STF), our proposed method effectively works in 5G-MEC task offloading scenario. For MILP, the dropped task ratio and delay has been minimized by 20% and 2ms compared to GA.

翻译：多接入边缘计算（MEC）作为一种可行的计算分配方法，能够将任务卸载至边缘服务器进行高效处理。MEC与5G的融合（称为5G-MEC）可在用户近端实现实时处理和数据驱动的决策制定。5G-MEC作为低延迟应用的关键工具，在任务卸载领域获得了显著认可。然而，目前鲜有研究关注任务丢弃率这一指标，忽略该指标可能损害系统效率。本文基于NS3实现的真实5G-MEC任务卸载场景，对任务丢弃率和时延进行了最小化处理。我们采用混合整数线性规划（MILP）与遗传算法（GA）优化时延与任务丢弃率，并系统考察了任务数量与用户数量对这两个指标的影响。相较于传统卸载方案（先来先服务FCFS与最短任务优先STF），本文提出的方法在5G-MEC任务卸载场景中表现出有效性能。实验表明，相较于GA算法，MILP方案将任务丢弃率降低20%，时延缩短2毫秒。