VNF Migration with Fast Defragmentation: A GAT-Based Deep Learning Method

TL;DR

This paper introduces a deep learning-based method using graph attention networks to optimize VNF migration and defragmentation in NFV networks, significantly improving resource utilization and reducing migration costs.

Contribution

It proposes a novel multi-hop graph attention network model for fast defragmentation and resource optimization in NFV, addressing multidimensional resource fragmentation.

Findings

Acceptance ratio increased by 12.8%.

Overload ratio decreased by 30.6%.

Migration loss reduced by 43.3%.

Abstract

Network function virtualization (NFV) enhances service flexibility by decoupling network functions from dedicated hardware. To handle time-varying traffic in NFV network, virtualized network function (VNF) migration has been involved to dynamically adjust resource allocation. However, as network functions diversify, different resource types may be underutilized due to bottlenecks, which can be described as multidimensional resource fragmentation. To address this issue, we firstly define a metric to quantify resource fragmentation in NFV networks. Then, we propose a multi-hop graph attention network (MHGAT) model to effectively extract resource features from tailored network layers, which captures the overall network state and produces high-quality strategies rapidly. Building on this, we develop an MHGAT method to implement fast defragmentation and optimize VNF migration. Simulations demonstrate that by fast defragmentation, the MHGAT method improves the acceptance ratio by an average of 12.8%, reduces the overload ratio by an average of 30.6%, and lowers migration loss by an average of 43.3% compared to the state-of-art benchmark.