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Fairness is a major issue in the operation of queues, perhaps it is the reason why queues were formed in the first place. Recent studies show that the fairness of a queueing system is important to customers not less than the actual delay they experience. Despite this observation little research has been conducted to study fairness in queues, and no commonly agreed upon measure of queue fairness exists. Two recent research exceptions are Avi-Itzhak and Levy [1], where a fairness measure is proposed, and Wierman and HarcholBalter [18] (this conference, 2003), where a criterion is proposed for classifying service policies as fair or unfair; the criterion focuses on customer service requirement and deals with fairness with respect to service times.In this work we recognize that the inherent behavior of a queueing system is governed by two major factors: Job seniority (arrival times) and job service requirement (service time). Thus, it is desired that a queueing fairness measure would account for both. To this end we propose a Resource Allocation Queueing Fairness Measure, (RAQFM), that accounts for both relative job seniority and relative service time. The measure allows accounting for individual job discrimination as well as system unfairness. The system measure forms a full scale that can be used to evaluate the level of unfairness under various queueing disciplines. We present several basic properties of the measure. We derive the individual measure as well as the system measure for an M/M/1 queue under five fundamental service policies: Processor Sharing (PS), First Come First Served (FCFS), Non-Preemptive Last Come First Served (NP-LCFS), Preemptive Last Come First Served (P-LCFS), and Random Order of Service (ROS). The results of RAQFM are then compared to those of Wierman and Harchol-Balter [18], and the quite intriguing observed differences are discussed.

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Polling systems have been used to model a large variety of applications and much research has been devoted to the derivation of efficient algorithms for computing the delay measures in these systems. Recent research efforts in this area, which have focused on the optimization of these systems, have raised the need for very efficient such algorithms.This work develops the descendant set approach as a general efficient algorithm for deriving all moments of customer delay (in particular, mean delay) in these systems. The method is applied to a very large variety of model variations, including:1) The exhaustive and gated service policies, 2) Fractional service policies, 3) The cyclic visit order, 4) Arbitrary periodic visit orders (polling tables), and 5) Customer routing. For mast of these variations the method significantly outperforms the algorithms commonly used today.

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