With the wide spread of various smart devices and the proliferation of internet of things (IoT) sensors, the amount of traffic on mobile networks is rapidly increasing, and applications with extreme requirements are increasing. Network function virtualization (NFV) and mobile edge computing (MEC) are emerging as core technologies to satisfy users’ real-time service demands. Adapting NFV technology to MECs allows the ability to assign cloud-computing capabilities near the base stations (BSs) of radio access networks (RANs), resulting in extremely fast service access to user equipment (UE). However, placement of virtualized network functions (VNF) within the edge network need to consider the location and the requirements of the user which change in real-time. There has been almost no consideration in the existing research on VNF resource allocation (VNF-RA) based on these aspects. Therefore, in this paper, a VNF resource allocation scheme based on context-aware grouping (VNF-RACAG) technology is proposed that enables groups (based on the geographic context of users, such as location and velocity) to compute the optimal number of clusters to minimize the end-to-end delay of network services. Then, a graph partitioning algorithm is used to minimize user movement between clusters, optimizing the data rate that users lose due to VNF migration.

中文翻译：

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5G边缘网络中基于上下文感知分组的集群虚拟化网络功能资源分配

随着各种智能设备的广泛普及和物联网（IoT）传感器的普及，移动网络上的流量正在迅速增加，具有极端要求的应用也越来越多。网络功能虚拟化（NFV）和移动边缘计算（MEC）正在成为满足用户实时服务需求的核心技术。将 NFV 技术应用于 MEC 允许在无线电接入网络 (RAN) 的基站 (BS) 附近分配云计算功能，从而实现对用户设备 (UE) 的极快服务接入。然而，虚拟化网络功能（VNF）在边缘网络中的放置需要考虑实时变化的用户位置和需求。现有的基于这些方面的VNF资源分配（VNF-RA）研究几乎没有考虑。因此，本文提出了一种基于上下文感知分组（VNF-RACAG）技术的VNF资源分配方案，使组（基于用户的地理上下文，如位置和速度）计算最优簇数以最小化网络服务的端到端延迟。然后，使用图分区算法最小化集群之间的用户移动，优化用户因 VNF 迁移而丢失的数据速率。例如位置和速度）来计算集群的最佳数量，以最小化网络服务的端到端延迟。然后，使用图分区算法最小化集群之间的用户移动，优化用户因 VNF 迁移而丢失的数据速率。例如位置和速度）来计算集群的最佳数量，以最小化网络服务的端到端延迟。然后，使用图分区算法最小化集群之间的用户移动，优化用户因 VNF 迁移而丢失的数据速率。