On Learning Bandwidth Allocation Models for Time-Varying Traffic in Flexible Optical Networks

We examine the problem of bandwidth allocation (BA) on flexible optical networks in the presence of traffic demand uncertainty. We assume that the daily traffic demand is given in the form of distributions describing the traffic demand fluctuations within given time intervals. We wish to find a predictive BA (PBA) model that infers from these distributions the bandwidth that best fits the future traffic demand fluctuations. The problem is formulated as a Partially Observable Markov Decision Process and is solved by means of Dynamic Programming. The PBA model is compared to a number of benchmark BA models that naturally arise after the assumption of traffic demand uncertainty. For comparing all the BA models developed, a conventional routing and spectrum allocation heuristic is used adhering each time to the BA model followed. We show that for a network operating at its capacity crunch, the PBA model significantly outperforms the rest on the number of blocked connections and unserved bandwidth. Most importantly, the PBA model can be autonomously adapted upon significant traffic demand variations by continuously training the model as real-time traffic information arrives into the network.