A Framework for Generalizing Uncertainty in Mobile Network Traffic Prediction

As Next Generation (NextG) networks become more complex, it has become increasingly necessary to utilize more advanced algorithms to enhance the robustness, autonomy, and reliability of existing wireless infrastructure. One such algorithm is network traffic prediction, playing a crucial role in the efficient operation of real-time and near-real-time network management. The contributions of this thesis are twofold. The first introduces a novel cluster-train-predict framework that leverages domain knowledge to identify unique timeseries sub-behaviors within aggregates of network data. This method produces distributions that are more robust towards changes in the spatio-temporal environment. The ensemble of time-series prediction models trained on these distributions posses a greater affinity towards accurate network prediction, selectively employing learned behaviors to handle sources of time-series data without any prior knowledge of it. This approach tends to improve the ability to accurately forecast network traffic volumes. Secondly, this thesis explains the development and implementation of a modular data pipeline to support the cluster-train-predict framework under a variety of conditions. This setup promotes repeatable and comparable results, facilitating rapid iteration and experimentation on current and future research. The results of this thesis surpass traditional approaches in [1] by up to 60%. Furthermore, the effectiveness of this framework is also validated using two additional time-series datasets [2] and [3], demonstrating the ability of this approach to generalize towards other time-series data and machine learning applications in uncertain environments.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Downey, Alexander Roman

Chair dc:contributor.committeechair

• Soysal, Alkan

Committee members dc:contributor.committeemember

• Xuan, Jianhua
• Reed, Jeffrey H.

• Network traffic prediction
• time-series forecasting
• time-series clustering
• deep learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40592

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119186