Dynamic Elastic Provisioning For NFV-Enabled 5G Networks Using Machine Learning

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dc.contributor.advisorJammal, Manar
dc.contributor.authorAli, Khalid
dc.date.accessioned2023-03-28T21:13:45Z
dc.date.available2023-03-28T21:13:45Z
dc.date.copyright2022-08-26
dc.date.issued2023-03-28
dc.date.updated2023-03-28T21:13:45Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstract5G 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.
dc.identifier.urihttp://hdl.handle.net/10315/40962
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subjectArtificial intelligence
dc.subject.keywordsOpen Radio Access Network
dc.subject.keywordsTime series
dc.subject.keywordsTraffic forecasting
dc.subject.keywordsScaling
dc.subject.keywordsPlacement
dc.subject.keywordsMachine Learning
dc.subject.keywordsReinforcement learning
dc.subject.keywordsElasticity
dc.subject.keywordsResource management
dc.subject.keywordsSelf-Organizing Network
dc.subject.keywordsNetwork Function Virtualization
dc.subject.keywordsARIMA
dc.subject.keywordsLSTM
dc.titleDynamic Elastic Provisioning For NFV-Enabled 5G Networks Using Machine Learning
dc.typeElectronic Thesis or Dissertation

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