Architectural and Cost Implications of the 5G Edge NFV Systems The recent issued 5G standard promises a comprehensive service provisioning for diverse user demands. The new challenges posed by 5G (e.g. the ultralow latency communication) urge the functionality deployment to offload from network core to edge. Besides, the various requirements of QoS guarantee cast by fastvarying services make the Network Function Virtualization (NFV) an essential enabler of edge network evolution. To date, the performance challenges at the newly created NFVenabled virtual network edge are still unexplored. In this paper, we investigate the converged virtual Radio Access Network (RAN) and Mobile Edge Computing (MEC) in 5G era. We first outline the solo virtual RAN performance. We then characterize the collocate workloads (RAN and MEC, RAN and RAN) on the commercial offtheshelf (COTS) edge platform. Finally, we provide brief cost comparison between traditional RAN and virtual RAN. The implications of the virtual RAN system will benefit future edge NFV design.

Reusing Subchains of Network Functions to Support MEC Services Mobile Edge Computing (MEC) and Network Function Virtualization (NFV) are widely considered to be key players in the 5G era. Whereas MEC enables to reduce latency significantly by allowing applications to be deployed close to end users, NFV allows for flexible deployment of virtualized network functions. The performance and flexibility can be improved further by combining MEC and NFV. Existing frameworks for managing and orchestrating MEC applications and NFV are either tightly coupled or completely separated. The former design is inflexible and increases the complexity of one framework, while the latter leads to inefficient use of computation resources. In this paper, we extend our Automated Provisioning Framework for MEC (APMEC), which combines each MEC application and its respective network service (comprising a chain of virtual network functions) in a MEC service. We propose a novel MEC service placement algorithm allowing to reuse a subset of yet underloaded network functions. Our evaluation results, obtained from a testbed implementation and simulations, show that our solution allows to remarkably increase the utilization of network functions and simultaneously reduce routing cost. Specifically, it allows to accept at least 60% more user requests, and at the same time lowering the routing cost by more than 30%, as compared to the baseline approach.