EmcFIS: Evolutionary multi-criteria Fuzzy Inference System for virtual network function placement and routing

Abstract

With the increasing demands for low-delay network services, mobile edge computing (MEC) has emerged as an appealing solution to provide computing resources in close to the end users. Network function virtualization (NFV) is a new network architecture which replaces dedicated hardware middleboxes with software instances to run network functions via software virtualization on general-purpose servers deployed at edge clouds. Because of the resource limitation at network edges, efficient placement and routing for online virtual network function requests (VNF-PRO) is a challenging task. The VNF-PRO has proven to be NP-hard, and thus, metaheuristic algorithms are the best choice in term of the solution quality. However, metaheuristics suffer from high computational complexity, and cannot be performed for online requests in the VNF-PRO. In this paper, a combined model based on fuzzy logic and genetic algorithm is proposed to achieve proper solution quality-speed trade-off in the VNF-PRO. In this method, a multi-criteria fuzzy inference system (named mcFIS) is used for the online VNF placement and routing. To achieve the best performance, a multi-objective evolutionary algorithm based on genetic algorithm (GA) is utilized in an offline procedure for automatic rule tuning of the mcFIS, once before applying it for online applications. Simulation results on two NFV benchmark instances demonstrate the efficiency of the proposed model against the existing techniques.