5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs.

5G technology will provide networks with high-bandwidth, low latency and multitenancy. The integration of computing and storage resources in the edge of the fronthaul network, i.e., multi-access edge computing (MEC), will allow to instantiate some virtual network functions (VNF) in those computing resources. The backhaul of 5G networks will be based on optical technology, in particular WDM, due to its high capacity and flexibility. In this paper, we analyse the problem of VNF-provisioning in a metro ring-topology network equipped with MEC resources and with a WDM network connecting the edge nodes. In contrast to previous proposals, the method decides where VNFs must be instantiated but also the design of the virtual topology for the WDM metro network in order to reduce the service blocking ratio and the number of resources in operation.

Network Function Virtualization (NFV) is considered to be one of the enabling technologies for 5G. NFV poses several challenges, like deciding the virtual network function (VNF) placement and chaining, and adding backup resources to guarantee the survivability of service chains. In this paper, we propose a genetic algorithm that jointly solves the VNF-placement, chaining and virtual topology design problem in WDM metro ring network, with the additional capacity of providing node protection. The simulation results show how important is to solve all of these subproblems jointly, as well as the benefits of using shared VNF and network resources between backup instances in order to reduce both the service blocking probability and the number of active CPUs.

A genetic algorithm is proposed to map virtual network functions in computing resources over 5G networks with an optical backhauling system. The algorithm outperforms other proposals in terms of blocking ratio and active CPU cores utilization.

A novel quality of transmission (QoT) estimator based on support vector machines (SVM) is proposed for classifying optical connections (lightpaths) into high or low quality categories in impairment-aware wavelength-routed optical networks (WRONs). The performance of the SVMbased estimator is evaluated in a long haul communications network and compared to previous semi-analytical and cognitive proposals. Results show that the SVM approach significantly reduces the necessary computing time to estimate the QoT of a given lightpath, critical aspect of design in these networks, and even slightly improves accuracy.