###### Abstract  
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; optical network; C-RAN; NFV; SDN; VNF; VNF-Provisioning; genetic algorithm; network planning; VNF Scaling +
Footnote †: journal: Accepted: 10 December 2018; Acceptedthe radio access network, but the metro and transport networks must be redefined from an architectural point of view. One of the most important enabling technologies for that redesign is the Centralized/Cloud Radio Access Networks (C-RAN) architecture. In C-RAN, the base station (BS) functionalities are divided into two separate entities: Remote Radio Heads (RRH) and Based Band Units (BBU). BBUs can be placed in the same location as RRH, but economical efficiencies are achieved when BBUs are placed at higher hierarchy premises (together with other BBUs, increasing the flexibility and reducing costs). In this architecture, the BBUs connect with the RRH through the so-called Mobile Fronthaul (MFH) segment and commonly implement the CPRI protocol to transport the traffic between RRHs and BBUs [2; 3]. Since the fronthaul traffic between RRHs and BBUs is usually the sampled, quantized and encoded radio signal [4; 5], it requires C-RAN to rely on networks with high capacity and guaranteed latencies. These restrictions constraint the location of the BBUs, e.g., to be hosted at the first aggregation site, although location at higher aggregation sites has been studied in Reference [6]. Moreover, 5G is expected to require CPRI rates significantly larger than the available bandwidth in current networks. For this reason, other functional splits, i.e., other divisions between the functionality that remains at the antenna site and the functionality deployed in the operator premises are considered to be deployed in 5G access networks [7]. In References [8; 9] the BBUs are divided into two entities: Distributed Unit (DU), which provides the physical layer processes and the real-time functionalities of the network, and the Central Unit (CU), which deploys non-real-time wireless high-level protocol function. Nonetheless, C-RAN latency and capacity requirements render optical technologies as the perfect candidate for the 5G fronthaul segments [2]. On the other hand, Mobile Edge Computing (MEC) technology will also contribute to the realization of 5G. MEC is a technology that provides cloud computing services at the edge of the network, i.e., closer to the end-user. Therefore, bringing the data processing to the edge node avoids the traffic to travel long distances incurring in propagation delays and, consequently, it can help to significantly reduce latency [10]. MEC and C-RAN seem to be colliding concepts, since MEC aims to distribute the data processing among the edge nodes of the network, whereas C-RAN aims to centralize the data processing in the BBU pools in the operator premises.