research-article

Distributed Proportional Fair Load Balancing in Heterogenous Systems

Authors:

Alexandre ProutiereAuthors Info & Claims

SIGMETRICS '15: Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

Pages 17 - 30

https://doi.org/10.1145/2745844.2745861

Published: 15 June 2015 Publication History

Abstract

We consider the problem of distributed load balancing in heterogenous parallel server systems, where the service rate achieved by a user at a server depends on both the user and the server. Such heterogeneity typically arises in wireless networks (e.g., servers may represent frequency bands, and the service rate of a user varies across bands). We assume that each server equally shares in time its capacity among users allocated to it. Users initially attach to an arbitrary server, but at random instants of time, they probe the load at a new server and migrate there if this improves their service rate. The dynamics under this distributed load balancing scheme, referred to as Random Local Search (RLS), may be interpreted as those generated by strategic players updating their strategy in a load balancing game. In closed systems, where the user population is fixed, we show that this game has pure Nash Equilibriums (NEs), and that these equilibriums get close to a Proportionally Fair (PF) allocation of users to servers when the user population grows large. We provide an anytime upper bound of the gap between the allocation under RLS and the PF allocation. In open systems, where users randomly enter the system and leave upon service completion, we establish that the RLS algorithm stabilizes the system whenever this it at all possible under centralized load balancing schemes, i.e., it is throughput-optimal. The proof of this result relies on a novel Lyapounov analysis that captures the dynamics due to both users' migration and their arrivals and departures. To our knowledge, the RLS algorithm constitutes the first fully distributed and throughput-optimal load balancing scheme in heterogenous parallel server systems. We extend our analysis to various scenarios, e.g. to cases where users can be simultaneously served by several servers. Finally we illustrate through numerical experiments the efficiency of the RLS algorithm.

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Nylander TKlein CArzen KMaggio M(2018) Brownout CC : Cascaded Control for Bounding the Response Times of Cloud Applications 2018 Annual American Control Conference (ACC)10.23919/ACC.2018.8431282(3354-3361)Online publication date: Jun-2018
https://doi.org/10.23919/ACC.2018.8431282
Talebi MProutiere A(2018)Learning Proportionally Fair Allocations with Low RegretProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/32244312:2(1-31)Online publication date: 13-Jun-2018
https://dl.acm.org/doi/10.1145/3224431
Borst SKaya ACalin DViswanathan H(2017)Dynamic path selection in 5G multi-RAT wireless networksIEEE INFOCOM 2017 - IEEE Conference on Computer Communications10.1109/INFOCOM.2017.8057228(1-9)Online publication date: May-2017
https://doi.org/10.1109/INFOCOM.2017.8057228
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Index Terms

Distributed Proportional Fair Load Balancing in Heterogenous Systems
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

SIGMETRICS '15: Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

June 2015

488 pages

ISBN:9781450334860

DOI:10.1145/2745844

General Chairs:
Bill Lin
University of California, San Diego
,
Jun (Jim) Xu
Georgia Tech
,
Program Chairs:
Sudipta Sengupta
Microsoft Research
,
Devavrat Shah
Massachusetts Institute of Technology

ACM SIGMETRICS Performance Evaluation Review Volume 43, Issue 1
Performance evaluation review
June 2015
468 pages
ISSN:0163-5999
DOI:10.1145/2796314
Editors:
Derek Eager
University of Saskatchewan
,
Carey Williamson
University of Calgary
Issue’s Table of Contents

Copyright © 2015 ACM.

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Published: 15 June 2015

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SIGMETRICS '15: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems

June 15 - 19, 2015

Oregon, Portland, USA

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SIGMETRICS '15 Paper Acceptance Rate 32 of 239 submissions, 13%;

Overall Acceptance Rate 459 of 2,691 submissions, 17%

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Cited By

Nylander TKlein CArzen KMaggio M(2018) Brownout CC : Cascaded Control for Bounding the Response Times of Cloud Applications 2018 Annual American Control Conference (ACC)10.23919/ACC.2018.8431282(3354-3361)Online publication date: Jun-2018
https://doi.org/10.23919/ACC.2018.8431282
Talebi MProutiere A(2018)Learning Proportionally Fair Allocations with Low RegretProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/32244312:2(1-31)Online publication date: 13-Jun-2018
https://dl.acm.org/doi/10.1145/3224431
Borst SKaya ACalin DViswanathan H(2017)Dynamic path selection in 5G multi-RAT wireless networksIEEE INFOCOM 2017 - IEEE Conference on Computer Communications10.1109/INFOCOM.2017.8057228(1-9)Online publication date: May-2017
https://doi.org/10.1109/INFOCOM.2017.8057228
Dandachi GElayoubi SChahed TChendeb N(2016)Network centric versus user centric multihoming strategies in LTE/WiFi networksIEEE Transactions on Vehicular Technology10.1109/TVT.2016.2597442(1-1)Online publication date: 2016
https://doi.org/10.1109/TVT.2016.2597442
Nguyen HSecci S(2016)Equilibrium routing: From theory to practice2016 23rd International Conference on Telecommunications (ICT)10.1109/ICT.2016.7500368(1-7)Online publication date: May-2016
https://doi.org/10.1109/ICT.2016.7500368
Chen LWu KLi Y(2014)A Load Balancing Algorithm Based on Maximum Entropy Methods in Homogeneous ClustersEntropy10.3390/e1611567716:11(5677-5697)Online publication date: 30-Oct-2014
https://doi.org/10.3390/e16115677
Wang HLiu GShen H(2022)Cooperative Job Scheduling and Data Allocation in Data-Intensive Parallel Computing ClustersIEEE Transactions on Cloud Computing10.1109/TCC.2022.3206206(1-14)Online publication date: 2022
https://doi.org/10.1109/TCC.2022.3206206
Tang DSubramanian V(2019)Random Walk Based Sampling for Load Balancing in Multi-Server SystemsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/3322205.33110853:1(1-44)Online publication date: 26-Mar-2019
https://dl.acm.org/doi/10.1145/3322205.3311085
Fossati FHoteit SMoretti SSecci S(2018)Fair Resource Allocation in Systems With Complete Information SharingIEEE/ACM Transactions on Networking10.1109/TNET.2018.287864426:6(2801-2814)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1109/TNET.2018.2878644
Fossati FMoretti SSecci S(2017)A mood value for fair resource allocations2017 IFIP Networking Conference (IFIP Networking) and Workshops10.23919/IFIPNetworking.2017.8264839(1-9)Online publication date: Jun-2017
https://doi.org/10.23919/IFIPNetworking.2017.8264839

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