Large-Scale Network Visualization

Visualization is increasingly important for understanding the structure of communication networks and services on them, but displaying the data associated with very large networks is di cult. There are abundant research problems in visualization metaphors, methods, algorithms and the engineering of scalable interactive systems. This area is being addressed in the AT&T Infolab, a multi-disciplinary project investigating visualization and analysis for AT&T's network and service businesses. The sheer magnitude of the data involved makes the challenge interesting: the voice network carries about 300 million calls per day, plus diagnostic information that is generated by the network itself; packet data networks such as frame relay, Asynchronous Transfer Mode (ATM) and Internet Protocol (IP) give rise to upward of millions of records daily, describing network conguration, dynamic behavior and events. The longrange goal for the Infolab work is to be able to provide a high-level, integrated view of all the AT&T networks, handling all the underlying data streams in real time. There are a variety of standard techniques for managing the display of large data sets. These include sampling the data, eliding or coalescing semantically chosen subsets, or relying on distorted views such as sheye[8] or hyperbolic[7] projections. Although these methods are useful, they clearly do not provide all the answers, especially when analyzing networkbased data. In this report, we describe two uncustomary approaches: a project in large-scale graphics and network displays, and an experiment with a novel graphical representation of networks.