As the Internet continues to evolve, without providing any performance
guarantees or explicit feedback to applications, the only way to infer the
state of the network and to dynamically react to congestion is through
end-to-end measurements. The emph{available bandwidth} (avail-bw) is an
important metric that characterizes the dynamic state of a network path.
Its measurement has been the focus of significant research during the last
15 years. However, its estimation remained elusive for several reasons.
The main contribution of this thesis is the development of the first
estimation methodology for the avail-bw in a network path using end-to-end
measurements. In more detail, our first contribution is an end-to-end
methodology, called SLoPS, to determine whether the avail-bw is larger
than a given rate based on the sequence of one-way delays experienced by a
periodic packet stream. The second contribution is the design of two
algorithms, based on SLoPS, to estimate the mean and the variation range,
respectively, of the avail-bw process. These algorithms have been
implemented in two measurement tools, referred to as PathLoad and PathVar.
We have validated the accuracy of the tools using analysis, simulation,
and extensive experimentation. Pathload has been downloaded by more than
6000 users since 2003. We have also used PathVar to study the variability
of the avail-bw process as a function of various important factors,
including traffic load and degree of multiplexing. Finally, we present an
application of avail-bw estimation in video streaming. Specifically, we
show that avail-bw measurements can be used in the dynamic selection of
the best possible overlay path. The proposed scheme results in better
perceived video quality than path selection algorithms that rely on jitter
or loss-rate measurements.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/22688 |
| Date | 09 April 2007 |
| Creators | Jain, Manish |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
Page generated in 0.0017 seconds