Fair opportunistic schedulers for Lossy Polling systems

TL;DR

This paper introduces a family of fairness-aware schedulers for lossy polling systems, accounting for travel conditions and system state, reducing utility disparities among stations, and analyzing the trade-offs involved.

Contribution

It proposes a novel family of opportunistic, fairness-aware schedulers for lossy polling systems that adapt to travel conditions and system state, extending existing scheduling frameworks.

Findings

Disparities in utilities diminish as fairness factor increases.

Price of fairness decreases with more stations.

Schedulers adapt to travel conditions and system state effectively.

Abstract

Polling systems with losses are useful mathematical objects that can model many practical systems like travelling salesman problem with recurrent requests. One of the less studied yet an important aspect in such systems is the disparity in the utilities derived by the individual stations. Further, the random fluctuations of the travel conditions can have significant impact on the performance. This calls for a scheduler that caters to the fairness aspect, depends upon the travel conditions and the dynamic system state. Inspired by the generalized alpha-fair schedulers of wireless networks, we propose a family of schedulers that further considers binary knowledge of the travel conditions. These schedulers are opportunistic, allocate the server to a station with bad travel condition only when the station has accumulated too little a utility by the decision epoch. We illustrate that the disparities among the individual utilities diminish to zero, as fairness factor increases, and further that the price of fairness decreases as the number of stations increase.