Deep learning-driven multi-objective dynamic switch migration in software defined networking (SDN)/network function virtualization (NFV)-based 5G networks

Abstract

This paper investigates the problem of switch migration in software defined networking (SDN)/network function virtualization (NFV)-based fifth generation (5G) networks in which SDN controllers are deployed as virtualized network functions (VNFs). The problem of switch migration is how to properly transfer the traffic of some SDN switches to one or multiple other virtualized SDN controllers in order to cope with the traffic fluctuations and the changing network topology. The highly dynamic environment of 5G networks requires the simultaneous consideration of multiple conflicting objectives in this problem including controllers load-balancing and network stability. In this paper, this multi-objective problem is formulated as a mixed integer non-linear program (MINLP) which is computationally expensive to solve. Therefore, a mathematical-based solution method is first provided based on the successive convex approximation (SCA) technique for the single-objective problem in which preference parameters are given to describe associated importance of the objectives. Taking into account the time restrictions for practical developments, an extended deep learning (DL) approach is then proposed to produce multiple mappings over multiple objectives. The verification results show that the proposed multi-objective DL algorithm can generate high quality Pareto Fronts close to those of the state-of-the-art algorithms based on optimization with orders of magnitude speedup in the computational time.