With advancements in networking technology and ubiquitous computing, there has been a significant increase in the number of edge devices and delay-sensitive applications. To facilitate efficient processing, mobile edge computing (MEC) technology provides resources through MEC servers, which are deployed at the radio access networks (RANs) of 5G networks. However, these MEC servers possess a limited amount of resources, making their effective management of these resources a critical challenge. This is due to the uneven distribution of resource utilization, where some resources become overutilized while others remain underutilized. Addressing the issue above while simultaneously satisfying user requirements for delay-sensitive applications poses a significant challenge at the edge. In this paper, we propose a clustering-based efficient RANs association and MEC server placement model to tackle this challenge. Our primary objective is to minimize MEC server deployment costs while ensuring that the delays of these applications are effectively managed. We propose a greedy algorithm called the clustering-based radio access networks association and mobile edge computing placement (CRAMP) algorithm, which determines the optimal location of MEC servers to associate with RANs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches regarding cost efficiency and delay management.

中文翻译：

---

CRAMP：基于集群的 RAN 关联和 MEC 布局，适用于延迟敏感型应用


随着网络技术和普适计算的进步，边缘设备和延迟敏感应用的数量显着增加。为了促进高效处理，移动边缘计算（MEC）技术通过部署在5G网络的无线接入网络（RAN）上的MEC服务器提供资源。然而，这些MEC服务器拥有的资源有限，这使得它们对这些资源的有效管理成为一个严峻的挑战。这是由于资源利用分布不均，一些资源被过度利用，而另一些资源则未被充分利用。解决上述问题，同时满足用户对延迟敏感的应用程序的要求，对边缘提出了重大挑战。在本文中，我们提出了一种基于集群的高效 RAN 关联和 MEC 服务器放置模型来应对这一挑战。我们的主要目标是最大限度地降低 MEC 服务器部署成本，同时确保有效管理这些应用程序的延迟。我们提出了一种称为基于集群的无线接入网络关联和移动边缘计算放置（CRAMP）算法的贪婪算法，该算法确定 MEC 服务器与 RAN 关联的最佳位置。仿真结果表明，我们提出的算法在成本效率和延迟管理方面优于现有方法。