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Clustered Virtualized Network Functions Resource Allocation based on Context-Aware Grouping in 5G Edge Networks


Using Big Data Analytics to Cr
Abstract


With the wide spread of various smart devices and the proliferation of 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 usersā?? 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.

KeyWords
Network functions virtualization (NFV), virtualized network function (VNF), mobile edge computing (MEC), 5G, VNF resource allocation, VNF migration, graph partitioning



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