Internet data traffic analysis for identifying usage trends on each day of the week in a university

Internet data traffic monitoring and management are important requirements for ensuring top notch quality of service in a network. Data traffic logs contain useful hidden information that can be harnessed and interpreted as a resource for making informed network management decisions. In this study, logged internet data traffic for both the upload and download traffic in a university for one year was analysed using statistics and partial least squares approach to structural equation modelling (PLS-SEM). Time series plots, statistical properties and trends for each day of the week over a 51-week period were developed. The result shows that the most data was downloaded on Thursdays while the most upload occurred on Mondays. A path model was developed using Smart PLS3, and the performance of the model was evaluated using the construct reliability and validity of the model. The results reveal that the weekly variance is majorly accounted for by usage variations on Tuesdays, Fridays and Saturdays. An overall model R-square value of 0.876 was observed.This is to express gratitude to Covenant University Centre for Research, Innovation and Discovery for the conducive research environment and publication support