Jammal, ManarAli, Khalid2023-03-282023-03-282022-08-262023-03-28http://hdl.handle.net/10315/409625G networks are expected to support a variety of services and applications by having a more stringent latency, reliability, and bandwidth requirements compared to previous generations. To meet these requirements, Open Radio Access Networks (O-RAN) has been proposed. The O-RAN Alliance assumes O-RAN components to be Virtualized Network Functions (VNFs). Furthermore, O-RAN allows employing Machine Learning (ML) solutions to tackle challenges in resource management. However, intelligently managing resources for O-RAN can prove challenging. Network providers need to dynamically scale resources in response to incoming traffic. Elastically allocating resources provides higher flexibility, reduces OPerational EXpenditure (OPEX), and increases resource utilization. In this work, we propose and evaluate an elastic VNF orchestration framework for O-RAN. The proposed system consists of a traffic forecasting-based dynamic scaling scheme using ML, and a Reinforcement Learning (RL) based VNF placement policy. The models are evaluated based on their predictive capabilities subject to all Service-Level Agreements.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Information technologyArtificial intelligenceElectronic Thesis or Dissertation2023-03-28Open Radio Access NetworkTime seriesTraffic forecastingScalingPlacementMachine LearningReinforcement learningElasticityResource managementSelf-Organizing NetworkNetwork Function VirtualizationARIMALSTM