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Rethinking Internet Traffic Management: From Multiple Decompositions to a Practical Protocol

Report ID:
January 2007
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In the Internet today, traffic management spans congestion control
(at end hosts), routing protocols (on routers) and traffic
engineering (by network operators). Historically, this division of
functionality slowly evolved without a conscious design. In this
paper, we perform a top-down redesign of traffic management using
recent innovations in optimization theory. First, we propose an
objective function that captures the goals of end users and network
operators alike. Using all known optimization decomposition
techniques, we generate four distributed algorithms where sources
adapt their sending rates along multiple paths, based on different
kinds of feedback from the links. Optimization theory guarantees
that these algorithms converge to a stable and optimal point, and
simulations allow us to compare rate of convergence, robustness to
tunable parameters, and performance under realistic traffic.
Combining the best features of the algorithms, we construct TRUMP: a
traffic management protocol that is distributed, adaptive, robust,
flexible and easy to manage. We show that using optimization
decompositions as a foundation, simulations as a building block, and
human intuition as a guide can be a principled approach to protocol

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