Optimal oblivious routing under statistical uncertainty

In telecommunication networks, a common measure is the maximum congestion (i.e., utilisation) on edge capacity. As traffic demands are often known with a degree of uncertainty, network management techniques must take into account traffic variability. The oblivious performance of a routing is a measure of how congested the network may get, in the worst case, for one of a set of possible traffic demands. We present two models to compute, in polynomial time, the optimal oblivious routing: a linear model to deal with demands bounded by box constraints, and a second-order conic program to deal with statistical uncertainty, i.e., when a mean-variance description of the traffic matrices is given. A comparison between the optimal oblivious routing and the well-known ospf routing technique on a set of real-world networks shows that, for different levels of uncertainty, optimal oblivious routing has a substantially better performance than ospf routing.