Quality of Experience Modeling for Fixed Broadband Internet Using Machine Learning Algorithms
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Date
2024-04
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Addis Ababa University
Abstract
As the demand for dependable fixed broadband internet services continues
to grow, ensuring an excellent Quality of Experience (QoE) for end-users is
essential. This thesis centers on QoE modeling, employing advanced machine
learning techniques, specifically Support Vector Machine (SVM) and
Random Forest algorithms. The study utilizes subjective assessments and
Quality of Service (QoS) metrics, including latency, upload speed, download
speed, uptime, packet loss, and jitter, to comprehensively comprehend and
model the factors influencing user satisfaction.The research incorporates an exhaustive feature selector to extract pertinentfeatures from the dataset, enhancing the precision of the models. Hyperparameteroptimization is carried out through a Grid Search approach tofine-tune the models for optimal performance. To assess the models, a robust cross-validation methodology is implemented. The results indicate that SVM surpasses Random Forest in QoE modeling for Virtual Internet Service Providers (vISPs) like Websprix and ZERGAW Cloud with average accuracy score of 92% and 70% respectively. Conversely, Random Forest proves to be the more suitable model for predicting QoE inthe case of the national ISP, ethio telecom with average accuracy value of88%. This comparative performance analysis offers valuable insights into thedistinct strengths of each model for different service providers.The research findings also indicate that employing both subjective and QoS metrics in combination to model the user QoE yields superior model performance and predictive outcomes compared to relying solely on subjective assessments and QoS metrics. These findings contribute to the ongoing discussion on QoE enhancement in fixed broadband internet services, providing practical recommendationsfor service providers based on observed model performances. The applicationof machine learning, feature selection, and hyperparameter optimizationtechniques underscores the importance of these methodologies in customizing QoE models to specific service contexts, ultimately enhancing user satisfaction in diverse fixed broadband Internet environments.
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Keywords
QoE, QoS, Support Vector Machine, Random Forest, vISP, Fixed Broadband Internet, Machine Learning Algorithm