Energy Efficient NFV Resource Allocation in Edge Computing Environment

With the development of IoT and 5G communication, a recent trend is to shift the Network Function Virtualization (NFV) from the centralized cloud computing to edge computing. In this paper, we study the energy efficient NFV-Resource Allocation problem in the edge computing environment. We define two problems. In the first problem, we assume that the physical resources (PRs) on the edge do not have energy constraint. Our objective is to find an optimal deployment so that the maximum energy consumption on the PRs is minimized. In the second problem, we assume that the PRs have energy constraint and aim to find an optimal deployment to reduce the number of PRs. We prove both problems NP-complete and propose heuristic algorithms to solve them. We also design baseline algorithms using genetic programming to find approximate optimal solutions to these problems. We conduct simulations to evaluate the performance of our proposed algorithms. Simulation results show that our algorithms produce results very close to those of the baseline algorithms in a much shorter time.