Automatically inferring patterns of resource consumption in network traffic

The Internet service model emphasizes flexibility -- any node can send any type of traffic at any time. While this design has allowed new applications and usage models to flourish, it also makes the job of network management significantly more challenging. This paper describes a new method of traffic characterization that automatically groups tra#c into minimal clusters of conspicuous consumption. Rather than providing a static analysis specialized to capture flows, applications, or network-to-network traffic matrices, our approach dynamically produces hybrid traffic definitions that match the underlying usage. For example, rather than report five hundred small flows, or the amount of TCP traffic to port 80, or the top ten hosts, our method might reveal that a certain percent of traffic was used by TCP connections between AOL clients and a particular group of Web servers. Similarly, our technique can be used to automatically classify new traffic patterns, such as network worms or peer-to-peer applications, without knowing the structure of such traffic a priori. We describe a series of algorithms for constructing these traffic clusters and minimizing their representation. In addition, we describe the design of our prototype system, AutoFocus and our experiences using it to discover the dominant and unusual modes of usage on several different production networks.