{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/29466"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/29466","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Machine Learning for Performance Aware Virtual Network Function Placement","abstract":"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).","abstract_html":"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).","abstract_has_math":false,"creators":["Manias, Dimitrios Michael"],"institution":"The University of Western Ontario","degree_name":"M Eng Sci","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Shami, Abdallah"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-27","date_published":"2019-08-27","updated_at":"2026-07-27T21:56:07Z","subjects":["Network Function Virtualization","Virtual Network Functions","Service Function Chain","VNF Placement","Machine Learning","Particle Swarm Optimization"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/29466","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Shami, Abdallah"]},{"key":"dc:creator","label":"Author","values":["Manias, Dimitrios Michael"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T18:32:04Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T18:32:04Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-08-27"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M Eng Sci"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Network Function Virtualization","Virtual Network Functions","Service Function Chain","VNF Placement","Machine Learning","Particle Swarm Optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/29466"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Collaborative Specialization: Artificial Intelligence","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["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)."]},{"key":"dc:title","label":"Title","values":["Machine Learning for Performance Aware Virtual Network Function Placement"]}]}],"canonical_facts":{"dc:contributor.advisor":["Shami, Abdallah"],"dc:creator":["Manias, Dimitrios Michael"],"dc:date.accessioned":["2025-07-10T18:32:04Z"],"dc:date.available":["2025-07-10T18:32:04Z"],"dc:date.issued":["2019-08-27"],"dc:description":["Collaborative Specialization: Artificial Intelligence","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["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)."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/29466"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["Network Function Virtualization","Virtual Network Functions","Service Function Chain","VNF Placement","Machine Learning","Particle Swarm Optimization"],"dc:title":["Machine Learning for Performance Aware Virtual Network Function Placement"],"dc:type":["thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["M Eng Sci"]},"updated_at":"2026-07-27T21:56:07Z"}