We present a thorough analysis of a commercial nationwide Wi-Fi hotspot network. The analysis is approached in two ways, characterization and... Show moreWe present a thorough analysis of a commercial nationwide Wi-Fi hotspot network. The analysis is approached in two ways, characterization and modeling. First we characterize the network from a ve month long log of user activ- ity and traffic collected by a wireless network service provider operating hotspots in restaurants, serviced apartments, hotels and airports all over Australia. The users are categorized based on their account time limits to analyze the impact of account strati cation on the overall user behavior. A similarity index is developed to com- pare two data sets. This is used to quantitatively measure how similar or different various types of accounts are. The user population in the network is found to be highly uctuating, hence user speci c, population independent metrics are proposed to manage this transience. We also introduce metrics to measure account time and data utilization. We then follow through with detailed modeling of session and traffic parame- ters. We develop the truncated loglogistic (T-LL) distribution which can model light and heavy tailed data using a modi cation of Lavalette's law. A novel method to t the T-LL distribution to data by minimizing a goodness-of- t metric is presented. The T-LL distribution and the tting method are subsequently used to model session and traffic parameters of the network based on the categorization methodology de- veloped previously. We address concerns about the speci city of the model by using it to model other publicly available Wi-Fi network traces. The property of the introduced T-LL distribution to model both light and heavy tailed data makes it uniquely quali ed for modeling web le sizes. Thus we extend the applicability of the introduced model by tting it to publicly available web le size data. The T-LL models outperform those of the Pareto and lognormal distributions used to model such data currently. Ph.D. in Computer Science, December 2014 Show less
