On scalable measurement-driven modeling of traffic demand in large WLANs

Abstract

Models of traffic demand are fundamental inputs to the design and engineering of data networks. In this paper we address this requirement in the context of large-scale wireless infrastructures using real measurement data from the University of North Carolina (UNC) wireless campus network. Our modeling effort focuses on capturing the demand variation in both the spatial and temporal domain in a way that scales well with the size of the wireless network. The network traffic dynamics are studied over two different week-long monitoring periods at various levels of spatial aggregation, from individual buildings to the whole network. We model traffic workload in terms of wireless sessions and network flows and find several modeling elements that are reusable in both temporal and spatial dimensions. The same set of parametric distributions for the session-and flow-related traffic variables capture the network traffic demand in both monitoring periods. Even more interestingly, these same distributions can characterize traffic dynamics at finer spatial scales, such as a single building or a group of buildings. We use our models to generate synthetic traffic and compare with trace data. The comparison clearly illustrates the trade-off between model scalability and reusability, on the one hand, and accuracy in capturing local-scale traffic dynamics on the other. Our main contribution is a novel behavioral approach for traffic demand modeling in large wireless networks that features high flexibility in the exploitation of the spatial and temporal resolution available in data traces.