Network Data Streaming – A Computer Scientist’s Journey in Signal Processing

August 22, 2008
Jun Xu | Georgia Tech

Accurate traffic measurement and monitoring is critical for network management and operation. With the rapid growth of the Internet, network link speeds have become faster every year to accommodate more Internet users. To accurately measure and monitor these high-speed links becomes a very challenging problem. Data streaming has been proposed as a viable solution for measuring and monitoring high-speed links in large networks. Data streaming is concerned with processing a long stream of data items in one pass using a small working memory in order to answer a class of queries regarding the stream. While data streaming has been studied by the database researchers, most of their results can not be directly applied to network data streaming. A key design challenge, which makes most database streaming algorithms inapplicable, is that the online processing of each data item (packet) has to finish within tens of nanoseconds, which is orders of magnitude more stringent than in database applications (on the order of milliseconds). To address this challenge, we developed a set of network data streaming algorithms that (1) has extremely low complexity so that it can keep up with high link speeds of future Internet and (2) accurately estimates various network statistics of interest.

Speaker Details

Jun (Jim) Xu is an Associate Professor in the College of Computing at Georgia Institute of Technology. He received his Ph.D. in Computer and Information Science from The Ohio State University in 2000. His current research interests include data streaming algorithms for the measurement and monitoring of computer networks, algorithms and data structures for computer networks, network security, and performance modeling and simulation. He received the NSF CAREER award in 2003 for his ongoing efforts in establishing fundamental lower bound and tradeoff results in networking. He is a co-author of a paper that won the Best Student Paper Award from 2004 ACM Sigmetrics/IFIP Performance joint conference, and the thesis advisor of the student winners. He is the proud recipient of IBM faculty awards in 2006 and 2008.