Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement

TL;DR

This paper introduces DO-DAT, a machine learning-based model utilizing particle swarm optimization for VNF placement, aiming to improve network performance and reduce costs in NFV environments.

Contribution

It presents a novel Depth-Optimized Delay-Aware Tree model optimized with particle swarm techniques for efficient VNF placement.

Findings

DO-DAT outperforms previous models in VNF placement accuracy

The model reduces network delay and operational costs

Experimental results validate the effectiveness of the approach

Abstract

With the constant increase in demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while ensuring continual improvements to network performance. Although Network Function Virtualization (NFV) has been identified as a solution, several challenges must be addressed to ensure its feasibility. In this paper, we present a machine learning-based solution to the Virtual Network Function (VNF) placement problem. This paper proposes the Depth-Optimized Delay-Aware Tree (DO-DAT) model by using the particle swarm optimization technique to optimize decision tree hyper-parameters. Using the Evolved Packet Core (EPC) as a use case, we evaluate the performance of the model and compare it to a previously proposed model and a heuristic placement strategy.