Balancing Efficiency and Fairness in Resource Allocation for Optical Networks

Traditionally, the bandwidth allocation problem is solved by maximizing network efficiency, which may however leave some connections unserved. This clearly leads to an unfair solution from the user's point of view, rendering fair bandwidth allocation algorithms of paramount importance, especially in the presence of congested network links. Specifically, fair bandwidth allocation algorithms are necessary to effectively control the achievable quality-of-service (QoS) of end-users, or equivalently to effectively control the fairness of the bandwidth allocation decisions. As the fair bandwidth allocation problem has long concerned network operators, various measures of fairness have been proposed. Amongst the most widely applied measures is the $\alpha $ -fair scheme that also captures proportional and max-min fairness by appropriately tuning the inequality aversion parameter $\alpha $ . Even though this scheme allows a network operator to control the arising fairness-efficiency trade-off, the extensive processing time required for iterating over several $\alpha $ -fair solutions often hinders its applicability. Committing, instead, to a single measure of fairness (e.g., proportional or max-min fairness) allows to fast approximate a fair bandwidth allocation but this may lead to either conservative QoS fairness levels or to a heavily degraded system efficiency. To alleviate limitations of known fairness measures, this work proposes a multi-objective optimization function that simultaneously optimizes both QoS fairness and network efficiency, aiming to derive an allocation that is as fair as possible to the extent that network utilization is not degraded for the sake of fairness. To evaluate the performance of the proposed function an optical network environment is considered where connections contend for the spectrum resources, demonstrating that the proposed optimization function significantly outperforms the $\alpha $ -fairness scheme in terms of processing time by up to 95% and its special cases in terms of fairness and system efficiency, thus alleviating also the limitations of committing to a single measure of fairness.