Optimized Service Chain Placement Using Genetic Algorithm

Abstract

Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service. Therefore, an effective service chain placement strategy is required to optimize the resource allocation and consequently to reduce the operating cost of the substrate network. To this end, we propose four genetic-based algorithms using roulette wheel and tournament selection techniques in order to place service chains considering two different placement strategies. Since mapping of service chains sequentially (One-at-a-time strategy) may lead to suboptimal placement, we also propose Simultaneous strategy that places all service chains at the same time to improve performance. Our goal in this work is to reduce deployment cost of VNFs while satisfying constraints. We consider Geant network as the substrate network along with its characteristics extracted from SndLib. The proposed algorithms are able to place service chains with any type of service graph. The performance benefits of the proposed algorithms are highlighted through extensive simulations.