Recently, edge user allocation (EUA) problem has received much attentions. It aims to appropriately allocate edge users to their nearby edge servers. Existing EUA approaches suffer from a series of limitations. First, considering users’ service requests only as a whole, they neglect the fact that in many cases a service request may be partitioned into multiple tasks to be performed by different edge servers. Second, the impact of the spatial distance between edge users and servers on users’ quality of experience is not properly considered. Third, the temporal dynamics of users’ service requests has not been fully considered. To overcome these limitations systematically, this article focuses on the problem of spatio-temporal edge user allocation with task decomposition (ST-EUA). We first formulate the ST-EUA problem. Then, we transform ST-EUA problem as an optimization problem with multiple objectives and global constraints and prove its NP\mathcal {NP}-hardness. To tackle the ST-EUA problem effectively and efficiently, we propose a novel genetic algorithm-based heuristic approach called GA-ST, aiming to maximize users’ overall QoE while minimizing the cost of task migration in different time slots. Extensive experiments are conducted on two widely-used real-world datasets to evaluate the performance of our approach. The results demonstrate that GA-ST significantly outperforms state-of-the-art approaches in finding approximate solutions in terms of the trade-off among multiple metrics.

中文翻译：

---

ST-EUA：具有任务分解的时空边缘用户分配


近年来，边缘用户分配（EUA）问题备受关注。它的目的是将边缘用户适当地分配到附近的边缘服务器。现有的 EUA 方法存在一系列限制。首先，仅将用户的服务请求视为一个整体，他们忽略了这样一个事实：在许多情况下，服务请求可能被划分为多个任务，由不同的边缘服务器执行。其次，没有适当考虑边缘用户与服务器之间的空间距离对用户体验质量的影响。第三，没有充分考虑用户服务请求的时间动态性。为了系统地克服这些限制，本文重点研究任务分解的时空边缘用户分配（ST-EUA）问题。我们首先制定 ST-EUA 问题。然后，我们将 ST-EUA 问题转化为具有多目标和全局约束的优化问题，并证明其 NP\mathcal {NP}-hardness。为了有效且高效地解决 ST-EUA 问题，我们提出了一种基于遗传算法的启发式方法，称为 GA-ST，旨在最大化用户的整体 QoE，同时最小化不同时隙中任务迁移的成本。在两个广泛使用的现实数据集上进行了大量的实验，以评估我们方法的性能。结果表明，在多个指标之间的权衡方面，GA-ST 在寻找近似解决方案方面明显优于最先进的方法。