A Machine Learning-Based Migration Strategy for Virtual Network Function Instances

TL;DR

This paper introduces VNNIM, a machine learning-based strategy for migrating Virtual Network Function instances, optimizing performance and efficiency in NFV environments, with high prediction accuracy and reduced run-time.

Contribution

The paper presents a novel neural network approach for VNF migration, enhanced by particle swarm optimization for hyperparameter tuning, improving prediction accuracy and efficiency.

Findings

99.07% binary accuracy in predicting post-migration servers

Centered delay difference distribution around zero

Significant run-time efficiency improvements

Abstract

With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased demand. Although Network Function Virtualization (NFV) has been identified as a promising solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) migration problem by developing the VNF Neural Network for Instance Migration (VNNIM), a migration strategy for VNF instances. The performance of VNNIM is further improved through the optimization of the learning rate hyperparameter through particle swarm optimization. Results show that the VNNIM is very effective in predicting the post-migration server exhibiting a binary accuracy of 99.07% and a delay difference distribution that is centered around a mean of zero when compared to the optimization model. The greatest advantage of VNNIM, however, is its run-time efficiency highlighted through a run-time analysis.