160 likes | 165 Vues
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.
E N D
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 ratex(t) Price feedback Optimization on the demand side: congestion control [Kelly-Maulloo-Tan ’98, Low-Lapsley ’99, many others] Book by Srikant, 2004.
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.
Price information: LINKS SOURCES
Traffic splitting Adapt splits LINKS SOURCES Node price recursion Adaptation of router traffic splits
Source 2 Destination Source 1 EXAMPLE Links in light blue have very high capacity.
EXAMPLE (cont) Fluid-flow simulation Using SCILAB
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.