Fernando%20Paganini

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Congestion control with
adaptive multipath routing based on optimization

• Fernando Paganini
• ORT University, Uruguay
• (on leave from UCLA)

Collaborator Enrique Mallada, ORT University,
Uruguay.
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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
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Combining demand and supply?
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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.