Optimal and near-optimal alpha-fair resource allocation algorithms based on traffic demand predictions for optical network planning

Abstract

We examine proactive network optimization in reconfigurable optical networks based on traffic predictions. Specifically, the fair spectrum allocation (SA) problem is examined for a priori reserving resources aiming to achieve near-even minimum quality-of-service (QoS) guarantees for all contending connections. The fairness problem arises when greedy SA policies are followed, which are based on point-based maximum demand predictions, especially under congested networks, with some connections highly overprovisioned and others entirely blocked, resulting in highly uneven QoS connection guarantees. To address this problem, we consider predictive traffic distributions allowing the exploration of several combinations of possible SAs. To find a fair SA policy, we resort to an $\\alpha$α-fairness scheme, while QoS fairness is evaluated according to a game-theoretic analysis based on the coefficient of variations of the connections’ unserved traffic metric, which measures the dispersion of unserved traffic for a connection around the mean of the unserved traffic over all connections. Non-contending (NC) and link-based contending (LBC) $\\alpha$α-fair SA integer linear programming algorithms are proposed, where the optimal NC approach encompasses global network contention information, while the near-optimal LBC approach encompasses partial contention information to reduce problem complexity. We show that as parameter $\\alpha$α increases, QoS fairness improves, along with connection blocking, resource utilization, and overprovisioning and underprovisioning. Further, LBC exhibits results close to the optimal, with a significant improvement in processing time.