Human activity modeling and Barabasi's queueing systems

TL;DR

This paper investigates how introducing aging mechanisms into priority queueing models affects task waiting time distributions, revealing that fat tails are not solely due to scheduling rules but also depend on dynamic priority changes.

Contribution

It analytically examines models with time-dependent priorities, demonstrating the impact of aging on waiting time distributions and contrasting with Barabasi's static priority models.

Findings

Aging mechanisms prevent fat tails from solely arising from scheduling rules.

Dynamic priorities assign high importance to long-waiting tasks.

Analytical characterization of waiting time distributions in aging models.

Abstract

It has been shown by A.-L. Barabasi that the priority based scheduling rules in single stage queuing systems (QS) generates fat tail behavior for the tasks waiting time distributions (WTD). Such fat tails are due to the waiting times of very low priority tasks which stay unserved almost forever as the task priority indices (PI) are "frozen in time" (i.e. a task priority is assigned once for all to each incoming task). Relaxing the "frozen in time" assumption, this paper studies the new dynamic behavior expected when the priority of each incoming tasks is time-dependent (i.e. "aging mechanisms" are allowed). For two class of models, namely 1) a population type model with an age structure and 2) a QS with deadlines assigned to the incoming tasks which is operated under the "earliest-deadline-first" policy, we are able to analytically extract some relevant characteristics of the the tasks waiting time distribution. As the aging mechanism ultimately assign high priority to any long waiting tasks, fat tails in the WTD cannot find their origin in the scheduling rule alone thus showing a fundamental difference between the present and the A.-L. Barabasi's class of models.