Network Data Streaming - A Computer Scientist's Journey in Signal Processing

GUEST LECTURER
Jun Xu

AFFILIATION
College of Computing, Georgia Institute of Technology

ABSTRACT
With the rapid growth of the Internet, network link speeds have
become faster every year to accommodate more Internet users. Measuring and
monitoring the traffic on such high-speed links has become an ever
challenging problem. Our team is among the first to realize that data
streaming algorithms have the potential to become very useful tools for
measuring and monitoring high-speed networks. Data streaming is concerned
with processing a long stream of data items in one pass using a small
working memory in order to estimate certain statistics of the stream. The
challenge is to use this small memory to "remember" as much information
pertinent to this estimation as possible. However, existing data streaming
algorithms are typically designed for processing a single stream of data
for a single type of statistic, and most of these algorithms cannot
operate at very high link speed. Targeting the deficiency of existing
approaches, we have investigated new data streaming paradigms and
mechanisms that allow us to perform large-scale distributed data streaming
on tens of thousands of high-speed links and nodes, and aggregate,
compress, and interpret these streaming results, for better measurement
and monitoring of large networks. We also discovered that the applications
of data streaming go far beyond network measurement and monitoring.
Recently, we have successfully applied the data streaming techniques to a
seemingly unrelated area: query routing in an unstructured P2P network. In
this talk, I will talk about these new applications as well as our theory
of formulating the data streaming problems in CS as the channel design
problem in EE.

DATE & TIME
Thursday, February 14, 2008 -- 11:00 AM

DURATION
1 hour

LOCATION
Executive Classroom