Robust regression for minimum-delay load-balancing

By providing origin-destination pairs with several possible paths, dynamic load-balancing has been shown to obtain excellent results in terms of robustness and effective resource usage. In these dynamic schemes, paths are defined a priori, and the portion of traffic routed through each path is (typically) adjusted so that the sum over all links of a certain link-cost function is minimized. Queueing delay is usually used as this cost function due to its versatility and simplicity. However, all load-balancing schemes require an analytical expression of the delay, for which oversimplistic models are used (such as the classic M/M/1 model). In this paper we propose a framework that instead learns this queueing delay function from measurements, while restricting the assumptions to the minimum. For this, we use a novel robust regression method that, given a set of link load and delay measurements, returns a very simple regression delay function. Some adjustments to this regression function allow us to use it as the link cost of a greedy load-balancing algorithm that converges to the actual minimum-delay configuration. We also compare our framework with previous load-balancing proposals, showing for instance that using the M/M/1 model results in a total delay that may easily exceed the minimum by 10%, and can go as high as more than 100%.