[Colloquium] Greenwald talk - Wed. Mar. 31st 2:45pm
Meridel Trimble
mtrimble at tti-c.org
Fri Mar 26 11:06:12 CST 2004
TOYOTA TECHNOLOGICAL INSTITUTE TALK
Speaker: Michael B. Greenwald
University of Pennsylvania
Speakers homepage: http://www.cis.upenn.edu/~mbgreen/
Time: 2:45pm
Date: Wednesday, March 31st
Place: TTI-C (1427 E. 60th St. 2nd Floor)
Refreshments provided
Title: AHBHA: Managing Congestion through Adaptive, Hop-By-Hop, Aggregation
Abstract:
The Internet may be among the most rapidly adopted technologies in history.
However, the speed of its adoption has enshrined what {\em is} at the expense
of what {\em ought to be}. Congestion control is particularly problematic:
although we no longer fear congestion collapse, existing congestion management
schemes are both brittle and complex. Brittle, because tools such as RED are
extremely sensitive to parameter settings. Complex, because many problems have
been addressed individually through distinct mechanisms that may interact in
unpredictable ways. Examples of such problems are utilization of large
bandwidth-delay product links, protection against non-congestion-aware flows,
defense against denial of service attacks, unfairness between competing flows,
interactions between "herds of mice" and "elephants", as well as others.
In this talk I describe three related projects. First, I describe the design
of AHBHA, a new congestion control architecture, and present some preliminary
results. AHBHA is based on hop-by-hop feedback, local flow aggregation, and
segregation of congested and non-congested flows. AHBHA addresses a large
number of problems with a single set of mechanisms.
The design of AHBHA required knowledge of actual congestion patterns in the
Internet. I describe {\tt cing}, a tool that can accurately capture delay
distributions on individual links in remote corners of the network without
requiring any extensions to the network's infrastructure.
To handle the vast number of observations returned by {\tt cing}, I present a
one-pass space-efficient algorithm that summarizes large quantities of
streaming data, while meeting a pre-specified guarantee on the accuracy of the
summary. This algorithm improves upon the worst-case space requirements of the
best known previous algorithms by a factor of $\Omega(\log N)$. Perhaps more
importantly, empirically, it seems to require constant space in almost all
realistic settings.
If you have questions, or would like to meet the speaker, please contact
Meridel at 4-9873/mtrimble at tti-c.org
For information on future TTI-C talks or events, please go to the TTI-C Events
page: http://www.tti-c.org/events.shtml
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