Dynamic Control of Running Servers

Esa Hyytiä*, Douglas Down, Pasi Lassila, Samuli Aalto

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Motivated by a data center setting, we study the problem of joint dispatching and server sleep state control in a system consisting of two queues in parallel. Using the theory of Markov decision processes and a novel lookahead approach, we explicitly determine near-optimal control policies that minimize a combination of QoE costs, energy costs, and wear and tear costs due to switching. Guidelines are provided as to when these combined policies are most effective.

Original languageEnglish
Title of host publicationMeasurement, Modelling and Evaluation of Computing Systems - 19th International GI/ITG Conference, MMB 2018, Proceedings
Pages127-141
Number of pages15
Volume10740 LNCS
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems - Erlangen, Germany
Duration: 26 Feb 201828 Feb 2018
Conference number: 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10740 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems
Abbreviated titleMMB
CountryGermany
CityErlangen
Period26/02/201828/02/2018

Keywords

  • Data centers
  • Dynamic control
  • Lookahead techniques
  • Markov decision processes
  • Queueing systems

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  • Cite this

    Hyytiä, E., Down, D., Lassila, P., & Aalto, S. (2018). Dynamic Control of Running Servers. In Measurement, Modelling and Evaluation of Computing Systems - 19th International GI/ITG Conference, MMB 2018, Proceedings (Vol. 10740 LNCS, pp. 127-141). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10740 LNCS). https://doi.org/10.1007/978-3-319-74947-1_9