1 research outputs found
A Machine Learning-Based Migration Strategy for Virtual Network Function Instances
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.Comment: Accepted - Future Technologies Conference 202