Machine Learning for Performance Aware Virtual Network Function Placement

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 connectivity demand. Although Network Function Virtualization has been identified as a potential solution, several challenges must be addressed to ensure its feasibility. The work presented in this thesis addresses the Virtual Network Function (VNF) placement problem through the development of a machine learning-based Delay-Aware Tree (DAT) which learns from the previous placement of VNF instances forming a Service Function Chain. The DAT is able to predict VNF instance placements with an average 34μs of additional delay when compared to the near-optimal BACON heuristic VNF placement algorithm. The DAT’s max depth hyperparameter is then optimized using Particle Swarm Optimization (PSO) and its performance is improved by an average of 44μs through the introduction of the Depth-Optimized Delay-Aware Tree (DO-DAT)