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Scalability and Robustness of Time-Series Databases for Cloud-Native Monitoring of Industrial Processes
ABB Corporate Research, Sweden.
ABB Corporate Research, Sweden.
ABB Corporate Research, Sweden.
ABB Corporate Research, Sweden.
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2014 (English)In: Proceedings 2014 IEEE Seventh International Conference on Cloud Computing CLOUD 2014, Alaska, United States, 2014, p. 602-609Conference paper, Published paper (Refereed)
Abstract [en]

Today’s industrial control systems store large amounts of monitored sensor data in order to optimize industrial processes. In the last decades, architects have designed such systems mainly under the assumption that they operate in closed, plant-side IT infrastructures without horizontal scalability. Cloud technologies could be used in this context to save local IT costs and enable higher scalability, but their maturity for industrial applications with high requirements for responsiveness and robustness is not yet well understood. We propose a conceptual architecture as a basis to designing cloud-native monitoring systems. As a first step we benchmarked three open source timeseries databases (OpenTSDB, KairosDB and Databus) on cloud infrastructures with up to 36 nodes with workloads from realistic industrial applications. We found that at least KairosDB fulfills our initial hypotheses concerning scalability and reliability.

Place, publisher, year, edition, pages
Alaska, United States, 2014. p. 602-609
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-28096DOI: 10.1109/CLOUD.2014.86OAI: oai:DiVA.org:mdh-28096DiVA, id: diva2:818254
Conference
IEEE Seventh Conference on Cloud Computing CLOUD2014, 27 Jun 27-2, 2014, Alaska, United States
Projects
InCloud - Indstrial Systems Cloud ComputingAvailable from: 2015-06-08 Created: 2015-06-08 Last updated: 2018-01-11Bibliographically approved

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Pei-Breivold, Hongyu

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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • Other locale
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Output format
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  • asciidoc
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