Towards Modeling of Traffic Demand of Node in Large Scale Network

Understanding actual network and traffic properties of the Internet is essential to determine network parameters in large-scale network simulations. However, there is little knowledge about the distribution of macroscopic traffic demand for each node, though the topological properties of the network have been focused on. This paper investigates the distribution of traffic volume to and from a node at an organization level. As traffic volume data, we used byte counter data of all interfaces in all backbone routers in a nation-wide research and education (R&E) network in Japan. First, we show that traffic volumes to and from a node in the network are characterized by a lognormal distribution, which has a slower decay than a normal distribution, but a faster decay than a power-law distribution. Thus, an assumption in which the traffic demand is uniformly random or Gaussian distributed is not appropriated to model the traffic demand in large-scale network simulation. This finding implies that one has more possibility to observe an increase of delay or packet drop in simulation, comparing to the result that uses uniformly-random or Gaussian traffic demand, because of the locality of traffic. Moreover, we observed that in 87% of nodes, a traffic volume from the backbone to the node is 1-10 times larger than that for the opposite direction. This is a similar usage pattern appeared in residential light-user broadband traffic. Finally, we introduce a simple model to explain the distribution of traffic demand, based on a multiplicative growth of traffic volume. We confirm that the multiplicative model can reproduce a lognormal distribution of traffic volume by simple numerical simulation.