Impact of Integrated Machine Learning Models, Background-Traffic and Bandwidth-Limit on the Performance of Software-Defined Networking

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dc.contributor.authorIsiaka Babatunde SADIKU
dc.date.accessioned2025-06-23T13:44:11Z
dc.date.available2025-06-23T13:44:11Z
dc.date.issued2024-12
dc.description.abstractEfficient data flow in computer networks is crucial for modern applications, but network performance faces challenges due to the complexity of network types and configurations. Understanding the impact of different networking approaches on packet flow, bandwidth, latency, jitter, and throughput is essential for improving network performance. Traditional Computer Networks (TCN) and emerging technologies like Software-Defined Networking (SDN) have distinct advantages and trade-offs in terms of bandwidth usage, latency, throughput, and jitter. This study aims to assess the influence of background traffic, bandwidth limits, and dataflow features on SDN performance and the ability of machine learning models to predict network behavior. The analysis reveals several key findings: Traditional networks exhibited higher throughput, while hybrid TCN-SDN showed reduced bandwidth usage. Latency varied across network types, with SDN networks showing potential increases. Jitter was significantly impacted by non-homogeneous networks, raising concerns about overall performance stability. ANOVA and Duncan’s tests confirmed the importance of latency, bandwidth, and throughput in influencing network behavior. Back-ground traffic and bandwidth limits were shown to have a complex relationship with SDN performance, particularly in terms of TCP bandwidth, throughput, and latency. Correlation analyses highlighted strong relationships between network parameters, providing deeper insights into dataflow dynamics. Among machine learning models, Support Vector Machine with Radial Basis Function Kernel (SVM_RBF) consistently outperformed others, while the stacked 5-stacked model demonstrated superior accuracy in predicting SDN performance across different datasets and scenarios. This study offers valuable insights into the interplay of network types, traffic conditions, and performance metrics. The results indicate that while traditional networks offer higher throughput, hybrid TCN-SDN configurations present advantages in bandwidth efficiency but may incur higher latency. The machine learning models successfully predicted network performance, with the 5-stacked model emerging as the most accurate across a range of conditions. Keywords: Performance Metrics, Programmable Network, Data Flow, Machine Learning, Bandwidth-traffic Word Count: 290 words
dc.identifier.citationKate Turabia
dc.identifier.otherP.hD
dc.identifier.urihttps://repository.lcu.edu.ng/handle/123456789/993
dc.language.isoen
dc.publisherLead City University, Ibadan
dc.relation.ispartofseriesP.hD
dc.subjectPerformance Metrics
dc.subjectProgrammable Network
dc.subjectData Flow
dc.subjectMachine Learning
dc.subjectBandwidth-traffic
dc.titleImpact of Integrated Machine Learning Models, Background-Traffic and Bandwidth-Limit on the Performance of Software-Defined Networking
dc.typeThesis

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