On Learning Bandwidth Allocation Models for Time-Varying Traffic in Flexible Optical Networks
Authors/Creators
- 1. KIOS Research and Innovation Center of Excellence, University of Cyprus
- 2. Department of Electrical Engineering, Computer Engineering, and Informatics Cyprus University of Technology
Description
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 realtime
traffic information arrives into the network.