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).

Name thesis:degree_name

M Eng Sci

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2019

Author dc:creator

• Manias, Dimitrios Michael

Advisor dc:contributor.advisor

• Shami, Abdallah

• Network Function Virtualization
• Virtual Network Functions
• Service Function Chain
• VNF Placement
• Machine Learning
• Particle Swarm Optimization

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/29466

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/29466