The following technical report is available from http://aib.informatik.rwth-aachen.de/:

Adaptive Routing with Stale Information Simon Fischer, Berthold Vöcking AIB 2005-06

We investigate adaptive routing policies for large networks in which agents reroute traffic based on old information. It is a well known and practically relevant problem that old information can lead to undesirable oscillation effects resulting in poor performance. We investigate how adaptive routing policies should be designed such that these effects can be avoided.

In our theoretical model, the network is represented by a general graph with latency functions on the edges. Traffic is managed by a large number of agents each of which is responsible for a negligible amount of traffic. Initially the agents' routing paths are chosen in an arbitrary fashion.

From time to time each agent revises her routing strategy by sampling

another path and switching with positive probability to this path if it promises smaller latencies. As the information on which the agent bases her decision might be stale, however, this does not necessarily lead to an improvement. The points of time at which agents revise their strategy are generated by a Poisson distribution. Stale information is modelled in form of a bulletin board that is updated periodically and lists the latencies on all edges.

We analyze such a distributed routing process in the so-called fluid limit, that is, we use differential equations describing the fractions of traffic on different paths over time. In our model, we can show the following effects. Simple routing policies that always switch to the better alternative lead to oscillation, regardless at which frequency the bulletin board is updated. Oscillation effects can be avoided, however, when using smooth adaption policies that do not always switch to better alternatives but only with a probability depending on the advantage in the latency. In fact, such policies have dynamics that converge to a fixed point corresponding to a Nash equilibrium for the underlying routing game, provided the update periods are not too large.

In addition, we also analyze the speed of convergence towards approximate equilibria of two specific variants of smooth adaptive routing policies, e.g., for a replication policy adopted from evolutionary game theory.