# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 2 Related Work
### Mobile Networks  
The authors in [46] present a novel meta-heuristic named Gray Wolf Optimizer algorithm (GWO), which is based on Particle Swarm Optimization (PSO). This approach aims to solve the problem of dynamic allocation of SDN controllers, which is modeled as a problem of multiple backpacks, in the context of Software Defined Mobile Network (SDMN). To this end, GWO was inspired by the behavior of gray wolf packs to find a solution to a given problem. Thus, like the pack, the algorithm was divided into four groups: alpha, beta, delta, and omega. The first is the leader and represents the best solution. Beta and delta represent, respectively, the second and third best solutions and the remaining members are defined as omega. When the pack finds a prey, it creates a circle around it and gradually decreases the size of the circle until the prey cannot move, this behavior is abstracted to find the optimal solution to the problem. Initially, the GWO algorithm generates a random population and iteratively updates the population calculating the cost and defining the members of the wolf groups. To improve the performance of the algorithm and in order to avoid optimal locations, the chaotic map technique is used to generate deterministic random sequences. Assuming that each group is a set of controllers and switches, it was necessary to run two instances of GWO nested. The first level determines the best number of controllers for each group and the second level identifies the best connections between switches and controllers. The best solution between the two levels is to use the least amount of controllers connected to the switches. In [47] is proposed a VNF placement and resource allocation (VNFPRA) problem. The authors devise as solutions two algorithms: a mixed integer program problem and a genetic based heuristic. Both solutions aim at reducing the overall placement and resource costs. The authors assume that just one VNF is not enough to meet the demands of all users' requests. Therefore, the replication of VNFs in other MEC nodes in the neighborhood is analyzed. A central controller with a global view of the network is used, which is responsible for deciding the optimal route for requesting VNFs. The results of the simulations indicate performance gains compared to solutions that already exist in the literature. The authors in [48] propose an architecture for the automatic allocation of VNFs using important technologies and frameworks in an IoV scenario (M-CORD, XOS, ONOS, OpenStack, and OpenAirInterface). An algorithm for decision making regarding elasticity was devised based on VNF resources utilization as a parameter. In [49] an extension of the MEC-NFV architecture for the allocation of SDN controllers is proposed. A testbed was implemented using OpenStack and Tacker to provide and manage the controller's life cycle. The main difference between [49] and this proposal is in the strategy adopted to decide the resources to be allocated to SDN controllers. In this proposal, the resources of each controller vary according to demand, whereas in [49] the controllers have the same amount of resources, changing only the number of controllers. In this proposal, a heuristic and an exact model are being demonstrated to optimize allocated resources on SDN controllers.