## 2 Related Work
[16] discussed MEC as a pathway for 5G realization, introduced different 5G-service scenarios and design challenges and proposed and explored the feasibility of MEC over Fiber Wireless Networks for different RAN scenarios, including the coexistence of MEC and C-RAN. Authors in References [17; 18] studied the integration of MEC and C-RAN from a resource allocation viewpoint, proposing techniques for resource allocation to reduce the energy consumption, while in Reference [19], authors proposed different coordination techniques to reduce the end-to-end latency in C-RAN with MEC. Lastly, [PERSON] et al. [20] explored the role that MEC, together with SDN and NFV will play to address the challenges 5G aims to undertake. None of the studies on the integration of MEC and C-RAN explores the use of NFV to deploy specific VNFs to offer final user services, such as video streaming, VoIP, or web browsing. The VNF placement and service chaining problem has also attracted the interest of the research community. In static scenarios, [PERSON] et al. [21] solved the resource allocation problem with a mixed-integer program to optimally serve end-to-end requests, minimizing CAPEX and OPEX and added a method to consider limited physical resources. In Reference [13], [PERSON] et al. studied the resource allocation and chaining problem considering the impact of IT resource-sharing among VNFs and the scalability issues that may appear when multiple SCs are deployed in a network through an ILP model. But the VNF placement problem is known to be NP-Hard and ILP models require large computation time to solve the problem for large networks [22]. Thus, heuristics and metaheuristics are employed to find sub-optimal solutions in polynomial time. For instance, authors in Reference [23] proposed a heuristic algorithm to study the impact of latency on VNF placement, showing that when the latency requirement of a service is strict, it can only be satisfied by deploying VNFs closer to the end user, in the metro/access network. Besides [PERSON] et al. [24] proposed a genetic algorithm for solving the VNF placement problem in Data Centers with the purpose of improving the load balancing. However, these efforts explore the VNF provisioning problem only on static scenarios. Several studies explore the VNF resource allocation problem in dynamic scenarios. For example, [PERSON] et al. [25] addressed the VNF-Provisioning problem in an Inter-Data Center Elastic OpticalNetwork for both static network planning and online provisioning through a Mixed-Integer Linear Programming (MILP) model, with the aim of reducing costs. More recently, authors in Reference [26] proposed an online algorithm to solve the VNF placement and chaining problem across geo-distributed datacenters, minimizing the operational costs. Authors in References [27; 28] explore the online VNF-Provisioning problem within datacenters. The first work proposes an algorithm to solve the VNF-Placement problem reducing provisioning costs and considering traffic rates between adjacent VNFs and server resource capacities, whereas the second study proposes an algorithm for service chain deployment and scaling that takes into consideration traffic fluctuations, to reduce deployment costs and link utilization. [PERSON] et al. [29] presented a resource allocation algorithm based on Genetic Algorithms to solve the VNF placement in a Data Center minimizing the usage of IT resources. [PERSON] et al. [30] presented an online VNF placement problem solving method based on genetic algorithms which aims to minimize the required time to obtain feasible solutions. Moreover, authors in Reference [31] addressed the resource allocation problem in an NFV system, which aims to minimize the system cost and maximize the number of served requests. Lastly, authors in References [32; 33] proposed a heuristic for online VNF placement and chaining in a realistic 5G scenario, aiming to reduce the service blocking rate, and taking into consideration computational resources and optical network capacity.