umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Power-aware cloud brownout: Response time and power consumption control
IDT, Mälardalen University, Sweden.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)ORCID iD: 0000-0001-8178-3921
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
Department of Automatic Control, Lund University, Sweden.
2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), IEEE, 2017, p. 2686-2691Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing infrastructures are powering most of the web hosting services that we use at all times. A recent failure in the Amazon cloud infrastructure made many of the website that we use on a hourly basis unavailable1. This illustrates the importance of cloud applications being able to absorb peaks in workload, and at the same time to tune their power requirements to the power and energy capacity offered by the data center infrastructure. In this paper we combine an established technique for response time control — brownout — with power capping. We use cascaded control to take into account both the need for predictability in the response times (the inner loop), and the power cap (the outer loop). We execute tests on real machines to determine power usage and response times models and extend an existing simulator. We then evaluate the cascaded controller approach with a variety of workloads and both open- and closed-loop client models.

Place, publisher, year, edition, pages
IEEE, 2017. p. 2686-2691
Keywords [en]
Cloud computing, Degradation, Power demand, Servers, Time factors, Virtual machining
National Category
Computer Systems
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:umu:diva-144722DOI: 10.1109/CDC.2017.8264049ISBN: 978-1-5090-2873-3 (electronic)ISBN: 978-1-5090-2872-6 (electronic)ISBN: 978-1-5090-2874-0 (print)OAI: oai:DiVA.org:umu-144722DiVA, id: diva2:1182083
Conference
2017 IEEE 56th Annual Conference on Decision and Control (CDC), December 12-15, 2017, Melbourne, Australia
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-06-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Krzywda, JakubElmroth, Erik

Search in DiVA

By author/editor
Krzywda, JakubElmroth, Erik
By organisation
Department of Computing Science
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 22 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf