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A resource-allocation queueing fairness measure

Published: 01 June 2004 Publication History

Abstract

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 Harchol-Balter [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.

References

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B. Avi-Itzhak and H. Levy. On measuring fairness in queues. Advances of Applied Probability, to appear, 2004.
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  • (2021)To Pool or Not to PoolOperations Research10.1287/opre.2019.197669:6(1866-1885)Online publication date: 1-Nov-2021
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Published In

cover image ACM SIGMETRICS Performance Evaluation Review
ACM SIGMETRICS Performance Evaluation Review  Volume 32, Issue 1
June 2004
432 pages
ISSN:0163-5999
DOI:10.1145/1012888
Issue’s Table of Contents
  • cover image ACM Conferences
    SIGMETRICS '04/Performance '04: Proceedings of the joint international conference on Measurement and modeling of computer systems
    June 2004
    450 pages
    ISBN:1581138733
    DOI:10.1145/1005686
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2004
Published in SIGMETRICS Volume 32, Issue 1

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Author Tags

  1. FCFS
  2. M/M/1
  3. PS
  4. fairness
  5. job scheduling
  6. processor sharing
  7. queue disciplines
  8. resource allocation
  9. unfairness

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  • (2023)HCoop: A Cooperative and Hybrid Resource Scheduling for Heterogeneous Jobs in Clouds2023 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom59040.2023.00046(238-245)Online publication date: 4-Dec-2023
  • (2022)ASTRAEA: A Fair Deep Learning Scheduler for Multi-Tenant GPU ClustersIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.313624533:11(2781-2793)Online publication date: 1-Nov-2022
  • (2021)To Pool or Not to PoolOperations Research10.1287/opre.2019.197669:6(1866-1885)Online publication date: 1-Nov-2021
  • (2020)Simple Near-Optimal Scheduling for the M/G/1Proceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/33794774:1(1-29)Online publication date: 5-Jun-2020
  • (2020)A Fair VNF Assignment Algorithm for Network Functions Virtualization2020 International Symposium on Networks, Computers and Communications (ISNCC)10.1109/ISNCC49221.2020.9297259(1-6)Online publication date: 20-Oct-2020
  • (2018)On slowdown variance as a measure of fairnessOperations Research Perspectives10.1016/j.orp.2018.05.0015(133-144)Online publication date: 2018
  • (2016)Autoscaling Effects in Speed Scaling Systems2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS.2016.48(307-312)Online publication date: Sep-2016
  • (2016)On fairness in polling systemsAnnals of Operations Research10.1007/s10479-016-2247-8317:1(253-285)Online publication date: 20-Jun-2016
  • (2015)On 2-moment completeness of non pre-emptive, non anticipative work conserving scheduling policies in some single class queues2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)10.1109/WIOPT.2015.7151082(267-274)Online publication date: May-2015
  • (2014)An Alternative View of Fairness in Petri NetsProceedings of the 2014 International C* Conference on Computer Science & Software Engineering10.1145/2641483.2641515(1-4)Online publication date: 3-Aug-2014
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