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Online Spike Detection in Cloud Workloads
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2015 (English)In: 2015 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E 2015), New York: IEEE Computer Society, 2015, 446-451 p.Conference paper, Published paper (Refereed)
Abstract [en]

We investigate methods for detection of rapid workload increases (load spikes) for cloud workloads. Such rapid and unexpected workload spikes are a main cause for poor performance or even crashing applications as the allocated cloud resources become insufficient. To detect the spikes early is fundamental to perform corrective management actions, like allocating additional resources, before the spikes become large enough to cause problems. For this, we propose a number of methods for early spike detection, based on established techniques from adaptive signal processing. A comparative evaluation shows, for example, to what extent the different methods manage to detect the spikes, how early the detection is made, and how frequently they falsely report spikes.

Place, publisher, year, edition, pages
New York: IEEE Computer Society, 2015. 446-451 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-125610DOI: 10.1109/IC2E.2015.50ISI: 000380449000072ISBN: 978-1-4799-8218-9 (print)OAI: oai:DiVA.org:umu-125610DiVA: diva2:1034090
Conference
2015 IEEE International Conference on Cloud Engineering, Arizona State University, Tempe, AZ, Mar 09-12, 2015.
Available from: 2016-10-11 Created: 2016-09-13 Last updated: 2016-10-11Bibliographically approved

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Mehta, AmardeepTordsson, JohanElmroth, Erik
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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