Abstract-With the advent of dynamic and elusive distributed applications such as peer-to-peer file sharing systems, network administrators find it increasingly difficult to understand the types of applications running in their networks and the amount of traffic each application produces.In this paper, we present measurement results from the deployment of an accurate traffic characterization application in three National Research and Education Networks for a period of two months. Our observations go beyond traffic distribution; we explore the application usage in terms of active IP addresses, the existence of IP addresses generating massive amounts of traffic, the asymmetry of incoming and outgoing traffic, and the existence of SPAM-sending mail servers.
Monitoring applications provide an important service in network related activities, such as network monitoring, network management and network software engineering. They facilitate the need of understanding exactly what occurs inside our networks and how each network interacts with the rest of the Internet. From private and local networks, to large-scale corporate networks and intranets, there is an ever-growing need to characterize and analyze network traffic. Unfortunately, network monitoring applications have the side effect of generating huge amounts of real-time data, that need to be processed, stored and presented, in an effective fashion. If this is done correctly and efficiently, network administrators, researchers, as well as users, can extract useful information from them, such as, traffic patterns, newly deployed network protocols, etc.In this paper we present our experiences on the combination of two tools, AppMon and Stager, and the study of the resulting system. AppMon is a network monitoring toolkit which performs per-application traffic classification. Stager is a tool which stores, aggregates and presents long-term network statistics, coming from multiple monitoring sides. We modified, combined, and extended these tools so that real-time data produced by AppMon are transferred, converted and stored through Stager. The resulting system gives access to valuable aggregated long-term network data which were not available through existing tools and methods.
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