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Fernando%20Paganini
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Title: Fernando%20Paganini
1
Congestion control with
adaptive multipath routing based on optimization
- Fernando Paganini
- ORT University, Uruguay
- (on leave from UCLA)
Collaborator Enrique Mallada, ORT University,
Uruguay.
2
Optimization on the demand side congestion
control
Kelly-Maulloo-Tan 98, Low-Lapsley 99, many
others Book by Srikant, 2004.
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Optimization on the supply side
4
Combining demand and supply?
5
Difficulties with the path formulation
- An exponential number of paths! How do we limit
size? - Sources do not have the path information, nor is
it reasonable to add all this complexity to them. - Overlay with the edge router doing rate control?
but even routers dont know end-to-end paths.
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A better set of control variables.
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More detailed notation
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Price information
LINKS
SOURCES
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Adaptation of router traffic splits
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Primal congestion control under adaptive
multipath routing
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Dual congestion control under fixed multipath
routing
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Dual congestion control under adaptive multipath
routing
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EXAMPLE
Links in light blue have very high capacity.
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EXAMPLE (cont)
Fluid-flow simulation Using SCILAB
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Implementation issues
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Conclusions
- We presented natural optimization problems that
combine multipath routing with elastic demands,
using variables which are local to sources and
routers. - We introduced congestion prices for nodes that
use multipath routing, and a slow adaptation of
traffic split ratios at routers. Combined with
standard congestion control, this strategy yields
decentralized solutions to the optimization
problems. - The algorithms fit with the TCP/IP philosophy
(end-to-end control of source rate, local control
of routing based on neighbor information). - Open question what happens if we remove
time-scale separations? - We are starting to look at implementation issues,
in particular combining explicit and implicit
methods to propagate prices.
