{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/43630"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/43630","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Hybrid AI-driven Approach to Context-Aware Inter-Slice Load Balancing for Cloud-Native Functions in 5G Networks","abstract":"This thesis explores transformative technologies in academia and industry, focusing on cloud-native or container-based network functions (CNFs) that improve scalability and lifecycle management compared to virtual network functions (VNFs). CNFs simplify orchestration by leveraging reliable service meshes and eliminating proxies. The thesis emphasizes high-level connectivity and APIs within custom execution environments (EEs), enabling AI-agent interactions for intelligent capabilities. Graph neural networks (GNN) and spatio-temporal multi-head graph attention networks (SP-mGAT) are utilized to generate context-aware embeddings, clustering clients by traffic characteristics into priority labels. These labels feed multi-agent deep reinforcement learning (MADRL) models like deep Q-networks (DQN) and proximal policy optimization (PPO) for traffic load balancing across core network slices, enhancing service quality. Kubernetes-integrated deployments use Prometheus for real-time predictive analytics, expanding beyond network-centric metrics to system-centric metrics. The hybrid platform achieves over 50% error reduction compared to earlier AI-assisted systems, demonstrating superior performance for intelligent, real-time decision-making in network environments.","abstract_html":"This thesis explores transformative technologies in academia and industry, focusing on cloud-native or container-based network functions (CNFs) that improve scalability and lifecycle management compared to virtual network functions (VNFs). CNFs simplify orchestration by leveraging reliable service meshes and eliminating proxies. The thesis emphasizes high-level connectivity and APIs within custom execution environments (EEs), enabling AI-agent interactions for intelligent capabilities. Graph neural networks (GNN) and spatio-temporal multi-head graph attention networks (SP-mGAT) are utilized to generate context-aware embeddings, clustering clients by traffic characteristics into priority labels. These labels feed multi-agent deep reinforcement learning (MADRL) models like deep Q-networks (DQN) and proximal policy optimization (PPO) for traffic load balancing across core network slices, enhancing service quality. Kubernetes-integrated deployments use Prometheus for real-time predictive analytics, expanding beyond network-centric metrics to system-centric metrics. The hybrid platform achieves over 50% error reduction compared to earlier AI-assisted systems, demonstrating superior performance for intelligent, real-time decision-making in network environments.","abstract_has_math":false,"creators":["Yaghoobi, Alireza"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Electrical and Computer","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T01:34:34Z","subjects":[],"languages":["en"],"rights":["Copyright © 2024 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.22215/etd/2025-16487"],"render_values":[{"text":"10.22215/etd/2025-16487","href":"https://doi.org/10.22215/etd/2025-16487","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14718/43630","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Yaghoobi, Alireza"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-05-23T20:05:31Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-23T20:05:31Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher","label":"Institution","values":["Carleton University"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering, Electrical and Computer"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (M.App.Sc.)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright © 2024 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.22215/etd/2025-16487"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14718/43630"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis explores transformative technologies in academia and industry, focusing on cloud-native or container-based network functions (CNFs) that improve scalability and lifecycle management compared to virtual network functions (VNFs). CNFs simplify orchestration by leveraging reliable service meshes and eliminating proxies. The thesis emphasizes high-level connectivity and APIs within custom execution environments (EEs), enabling AI-agent interactions for intelligent capabilities. Graph neural networks (GNN) and spatio-temporal multi-head graph attention networks (SP-mGAT) are utilized to generate context-aware embeddings, clustering clients by traffic characteristics into priority labels. These labels feed multi-agent deep reinforcement learning (MADRL) models like deep Q-networks (DQN) and proximal policy optimization (PPO) for traffic load balancing across core network slices, enhancing service quality. Kubernetes-integrated deployments use Prometheus for real-time predictive analytics, expanding beyond network-centric metrics to system-centric metrics. The hybrid platform achieves over 50% error reduction compared to earlier AI-assisted systems, demonstrating superior performance for intelligent, real-time decision-making in network environments."]},{"key":"dc:title","label":"Title","values":["Hybrid AI-driven Approach to Context-Aware Inter-Slice Load Balancing for Cloud-Native Functions in 5G Networks"]}]}],"canonical_facts":{"dc:creator":["Yaghoobi, Alireza"],"dc:date.accessioned":["2025-05-23T20:05:31Z"],"dc:date.available":["2025-05-23T20:05:31Z"],"dc:date.issued":["2025"],"dc:description.abstract":["This thesis explores transformative technologies in academia and industry, focusing on cloud-native or container-based network functions (CNFs) that improve scalability and lifecycle management compared to virtual network functions (VNFs). CNFs simplify orchestration by leveraging reliable service meshes and eliminating proxies. The thesis emphasizes high-level connectivity and APIs within custom execution environments (EEs), enabling AI-agent interactions for intelligent capabilities. Graph neural networks (GNN) and spatio-temporal multi-head graph attention networks (SP-mGAT) are utilized to generate context-aware embeddings, clustering clients by traffic characteristics into priority labels. These labels feed multi-agent deep reinforcement learning (MADRL) models like deep Q-networks (DQN) and proximal policy optimization (PPO) for traffic load balancing across core network slices, enhancing service quality. Kubernetes-integrated deployments use Prometheus for real-time predictive analytics, expanding beyond network-centric metrics to system-centric metrics. The hybrid platform achieves over 50% error reduction compared to earlier AI-assisted systems, demonstrating superior performance for intelligent, real-time decision-making in network environments."],"dc:identifier.doi":["10.22215/etd/2025-16487"],"dc:identifier.uri":["https://hdl.handle.net/20.500.14718/43630"],"dc:language.iso":["en"],"dc:publisher":["Carleton University"],"dc:rights":["Copyright © 2024 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner."],"dc:title":["Hybrid AI-driven Approach to Context-Aware Inter-Slice Load Balancing for Cloud-Native Functions in 5G Networks"],"dc:type":["thesis"],"thesis:degree_discipline":["Engineering, Electrical and Computer"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Applied Science (M.App.Sc.)"]},"updated_at":"2026-07-24T01:34:34Z"}