Hyper-scalable JSQ with sparse feedback

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Load balancing algorithms play a vital role in enhancing performance in data centers and cloud networks. Due to the massive size of these systems, scalability challenges, and especially the communication overhead associated with load balancing mechanisms, have emerged as major concerns. Motivated by these issues, we introduce and analyze a novel class of load balancing schemes where the various servers provide occasional queue updates to guide the load assignment. We show that the proposed schemes strongly outperform JSQ(d) strategies with comparable communication overhead per job, and can achieve a vanishing waiting time in the many-server limit with just one message per job, just like the popular JIQ scheme. The proposed schemes are particularly geared however towards the sparse feedback regime with less than one message per job, where they outperform corresponding sparsified JIQ versions. We investigate fluid limits for synchronous updates as well as asynchronous exponential update intervals. The fixed point of the fluid limit is identified in the latter case, and used to derive the queue length distribution. We also demonstrate that in the ultralow feedback regime the mean stationary waiting time tends to a constant in the synchronous case, but grows without bound in the asynchronous case.

Original languageEnglish
Title of host publicationSIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages61-62
Number of pages2
ISBN (Electronic)978-1-4503-6678-6
DOIs
Publication statusPublished - 20 Jun 2019
Event14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019 - Phoenix, United States
Duration: 24 Jun 201928 Jun 2019

Conference

Conference14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019
CountryUnited States
CityPhoenix
Period24/06/1928/06/19

Fingerprint

Resource allocation
Feedback
Servers
Fluids
Communication
Scalability

Keywords

  • Cloud networks
  • Data centers
  • Delay performance
  • Join-the-shortest-queue
  • Load balancing
  • Parallelserver systems
  • Scaling limits

Cite this

van der Boor, M., Borst, S., & van Leeuwaarden, J. (2019). Hyper-scalable JSQ with sparse feedback. In SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems (pp. 61-62). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/3309697.3331477
van der Boor, Mark ; Borst, Sem ; van Leeuwaarden, Johan. / Hyper-scalable JSQ with sparse feedback. SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. New York : Association for Computing Machinery, Inc, 2019. pp. 61-62
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van der Boor, M, Borst, S & van Leeuwaarden, J 2019, Hyper-scalable JSQ with sparse feedback. in SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery, Inc, New York, pp. 61-62, 14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019, Phoenix, United States, 24/06/19. https://doi.org/10.1145/3309697.3331477

Hyper-scalable JSQ with sparse feedback. / van der Boor, Mark; Borst, Sem; van Leeuwaarden, Johan.

SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. New York : Association for Computing Machinery, Inc, 2019. p. 61-62.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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van der Boor M, Borst S, van Leeuwaarden J. Hyper-scalable JSQ with sparse feedback. In SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. New York: Association for Computing Machinery, Inc. 2019. p. 61-62 https://doi.org/10.1145/3309697.3331477