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Real-time resource prediction engine for cloud management
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2017 (English)In: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 877-878Conference paper, Published paper (Refereed)
Abstract [en]

Predicting resource requirements for cloud services is critical for dimensioning, anomaly detection and service assurance. We demonstrate a system for real-time estimation of the needed amount of infrastructure resources, such as CPU and memory, for a given service. Statistical learning methods on server statistics and load parameters of the service are used for learning a resource prediction model. The model can be used as a guideline for service deployment and for real-time identification of resource bottlenecks. © 2017 IFIP.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. p. 877-878
Keywords [en]
Cloud managements, Infrastructure resources, Real-time estimation, Real-time identification, Resource prediction, Resource requirements, Service deployment, Statistical learning methods, Forecasting
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-216282DOI: 10.23919/INM.2017.7987392Scopus ID: 2-s2.0-85029437876ISBN: 9783901882890 OAI: oai:DiVA.org:kth-216282DiVA, id: diva2:1165640
Conference
15th IFIP/IEEE International Symposium on Integrated Network and Service Management, IM 2017, 8 May 2017 through 12 May 2017
Note

QC 20171213

Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-13Bibliographically approved

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Stadler, Rolf

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • 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