VNF Placement with Replication for Load Balancing in NFV Networks

TL;DR

This paper investigates VNF placement with replication in NFV networks to improve load balancing, comparing optimization methods including LP, GA, and RFPA, and proposing a GA heuristic for larger networks.

Contribution

It introduces a comprehensive study of VNF placement with replication, proposing and comparing three optimization methods, and developing a GA heuristic for scalable load balancing solutions.

Findings

Optimal placement and replication significantly improve load balancing.

GA heuristic performs well on larger networks.

LP and GA outperform random placement methods.

Abstract

Network Function Virtualization (NFV) is a new paradigm, enabling service innovation through virtualization of traditional network functions located flexibly in the network in form of Virtual Network Functions (VNFs). Since VNFs can only be placed onto servers located in networked data centers, which is the NFV's salient feature, the traffic directed to these data center areas has significant impact on network load balancing. Network load balancing can be even more critical for an ordered sequence of VNFs, also known as Service Function Chains (SFCs), a common cloud and network service approach today. To balance the network load, VNF's can be placed in a smaller cluster of servers in the network thus minimizing the distance to the data center. The optimization of the placement of these clusters is a challenge as also other factors need to be considered, such as the resource utilization. To address this issue, we study the problem of VNF placement with replications, and especially the potential of VNFs replications to help load balance the network. We design and compare three optimization methods, including Linear Programing (LP) model, Genetic Algorithm (GA) and Random Fit Placement Algorithm (RFPA) for the allocation and replication of VNFs. Our results show that the optimum placement and replication can significantly improve load balancing, for which we also propose a GA heuristics applicable to larger networks.