talk

Data-Driven Network Analysis: Do You Really Know Your Data?

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July 15, 2009

A "dirty little secret" of network measurements is that what we can measure is often not what we want to (or think we) measure. To illustrate, Walt will discuss some of the main problems and challenges associated with analyzing and modeling measurements collected for the purpose of inferring certain types of Internet-related connectivity structures (e.g., a network provider's physical infrastructure or router-level topology). In particular, Walt will demonstrate with some concrete examples the need to (i) understand the process by which Internet connectivity measurements are obtained, (ii) explore the sensitivity of inferred graph properties to known ambiguities in the data, and (iii) be more serious/ambitious when it comes to model validation. Ignoring any of these issues is bound to lead to specious models (e.g., preferential attachment-type network models) that quickly collapse when scrutinized by domain experts.

Presentation Slides (PPT)

Speaker

Walter Willinger

Walter Willinger, a member of the Information and Software Systems Research Center at AT&T Labs Research in Florham Park, NJ, has been a leading researcher into the self-similar ("fractal") nature of Internet traffic. His paper "On the Self-Similar Nature of Ethernet Traffic" is featured in "The Best of the Best - Fifty Years of Communications and Networking Research," a 2007 IEEE Communications Society book compiling the most outstanding papers published in the communications and networking field in the last half century. More recently, he has focused on investigating the topological structure of the Internet and on developing a theoretical foundation for the study of large-scale communication networks such as the Internet.