1 / 16

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

Congestion control with adaptive multipath routing based on optimization. Fernando Paganini ORT University, Uruguay (on leave from UCLA). Collaborator: Enrique Mallada, ORT University, Uruguay. Source rate x (t). Price feedback.

dhillis
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. Source ratex(t) Price feedback Optimization on the demand side: congestion control [Kelly-Maulloo-Tan ’98, Low-Lapsley ’99, many others] Book by Srikant, 2004.

  3. 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 don’t know end-to-end paths.

  6. A better set of control variables.

  7. More detailed notation:

  8. Price information: LINKS SOURCES

  9. Traffic splitting Adapt splits LINKS SOURCES Node price recursion Adaptation of router traffic splits

  10. Primal congestion control under adaptive multipath routing:

  11. Dual congestion control under fixed multipath routing:

  12. Dual congestion control under adaptive multipath routing:

  13. Source 2 Destination Source 1 EXAMPLE Links in light blue have very high capacity.

  14. EXAMPLE (cont) Fluid-flow simulation Using SCILAB

  15. Implementation issues

  16. 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.

More Related