Multi-Access Edge Computing (MEC) is widely recognized as an essential enabler for applications that necessitate minimal latency. However, the dropped task ratio metric has not been studied thoroughly in literature. Neglecting this metric can potentially reduce the system's capability to effectively manage tasks, leading to an increase in the number of eliminated or unprocessed tasks. This paper presents a 5G-MEC task offloading scenario with a focus on minimizing the dropped task ratio, computational latency, and communication latency. We employ Mixed Integer Linear Programming (MILP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to optimize the latency and dropped task ratio. We conduct an analysis on how the quantity of tasks and User Equipment (UE) impacts the ratio of dropped tasks and the latency. The tasks that are generated by UEs are classified into two categories: urgent tasks and non-urgent tasks. The UEs with urgent tasks are prioritized in processing to ensure a zero-dropped task ratio. Our proposed method improves the performance of the baseline methods, First Come First Serve (FCFS) and Shortest Task First (STF), in the context of 5G-MEC task offloading. Under the MILP-based approach, the latency is reduced by approximately 55% compared to GA and 35% compared to PSO. The dropped task ratio under the MILP-based approach is reduced by approximately 70% compared to GA and by 40% compared to PSO.

翻译：多接入边缘计算（MEC）被广泛认为是实现超低延迟应用的关键使能技术。然而，现有文献对任务丢弃率这一指标的研究尚不充分。忽略该指标可能会削弱系统有效管理任务的能力，导致被丢弃或未处理任务数量的增加。本文提出一种面向5G-MEC的任务卸载场景，重点优化任务丢弃率、计算延迟与通信延迟。我们采用混合整数线性规划（MILP）、粒子群优化（PSO）和遗传算法（GA）对延迟与任务丢弃率进行联合优化。系统分析了任务数量与用户设备（UE）数量对任务丢弃率及延迟的影响机制。由UE生成的任务被划分为紧急任务与非紧急任务两类，优先处理携带紧急任务的UE以确保其任务零丢弃率。在5G-MEC任务卸载场景中，本研究所提方法相较于先到先服务（FCFS）与最短任务优先（STF）等基线方法表现出更优性能。基于MILP的方案相较于GA降低约55%的延迟，相较于PSO降低约35%的延迟；在任务丢弃率方面，MILP方案较GA降低约70%，较PSO降低约40%。