Understanding the characteristics of Internet traffic dynamics in wired and wireless networks

The objective of this dissertation is to characterize and model traffic dynamics of wired and wireless internet for supporting effective deployment and operations of commercial communications networks. As the internet has become the prevalent network infrastructure for integrating broadcasting, telecommunications, and data communications, multimedia services driven by internet service providers (ISPs) have proliferated. Mobile internet services have become popular as mobile devices such as smartphones or tablets are pervasively used. As a result, we have seen persistent growth of internet traffic in wired and wireless networks. ❧ To cope with the growth of the data traffic volume generated by these mobile devices network operators need to increase network capacity, and design and operate their networks effectively. To increase the network capacity according to the forecasted traffic demand is not always cost-effective as the traffic usage patterns can change in unanticipated ways with the development of new applications. Furthermore it is not easy for mobile network operators to increase their network capacity according to the forecasted demand due to scarce radio frequency spectrum. The network operators are required to build their networks efficiently and optimize them according to the characteristics of usage and traffic behavior. To this end, the first step is to understand the spatial and temporal patterns of the internet traffic carried by their networks. ❧ In this dissertation, we perform extensive traffic collection, analysis and modeling of wired and wireless internet traffic to enhance network deployment and operational efficiency. We collected traffic datasets from a commercial network operator covering diverse services, times and locations. We characterize the variability and dynamics of internet traffic to identify inherent characteristics and combine statistical and empirical methodologies to model the identified traffic behaviors using appropriate parametric models. We also verify the effectiveness of suggested solutions to address several problems recently encountered in wired and wireless networks. ❧ Our results are helpful to understand traffic behavior in wired and wireless networks and to demystify traffic characteristics in commercial networks. The statistical models we found can also be used to find generative models for synthetic traffic generators in simulation or analytical studies. We believe that the solutions verified in this research can be applied to today’s networks including long term evolution (LTE) networks.