This paper proposes a methodology for the efficient allocation of both optical and wireless resources in an SDN/NFV-based converged optical-wireless network architecture. Our approach considers a network slicing architecture where different network slices form end-to-end logically isolated networks, each one dedicated to a different type of service with diverse requirements. The target of the proposed approach is to optimally determine the network slices so that the specific delay and bandwidth requirements of the multiple services are met, by considering both the optical and wireless network resources.

Network Function Virtualization (NFV)-based 5G networks deliver specific services to a set of users through the creation of Service Function Chains (SFCs), which are composed of a number of Virtualized Network Functions (VNFs) interconnected via a set of virtual links (vLinks). VNFs consume computational resources of the network's servers, while vLinks utilize the communicational resources of the network. The efficient utilization of network resources remains a challenge in NFV-based networks. To this end, in this paper, we propose an analytical framework for the calculation of the Admission Ratio (AR) in NFV-based converged optical-wireless 5G networks. The proposed methodology employs a network slicing architecture in which different network slices form end-to-end logically isolated networks and each slice delivers a specific service type to the users through its SFC(s). In the proposed analytical model, we not only take into account the occupancy distribution in both computational and communicational domains of the network resources (servers and fiber links), but we also consider the SFC establishment AR by taking into account different subservice-classes belonging to different slices. The accuracy of the model is evaluated through the comparison of analytical and simulation results and was found satisfactory. Furthermore, the network dimensioning is performed by employing the proposed model for the determination of the optimal (minimum) capacity for all SFCs elements (VNFs and vLinks) in a way that users belonging to a specific slice experience a predefined value of AR as minimum. Additionally, our calculations deploy recursive formulas, which have a low computational complexity, as opposed to time-consuming simulation approaches without requiring the application of complex optimization algorithms.

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