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Analyzing availability and QoS of service-oriented cloud for industrial IoT applications
RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.ORCID iD: 0000-0002-3375-6766
RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.ORCID iD: 0000-0002-6657-2496
RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
2019 (English)In: 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2019, p. 1403-1406Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things and cloud services are one of main enablers in fourth industrial revolution. Real-time industrial systems have high availability requirements of 99.9% to 99.999% whereas architectures built on regional cloud services and IoT do not provide similar guarantees or Service Level Agreement. These differences of QoS and SLA availability between Operational Technology and Information Technology has become a main challenge in adoption of Industrial Internet of Things (IIoT) for real-time applications.This work presents an approach to find end-to-end QoS and availability for an IIoT architecture. Device-to-cloud, cloud-to-cloud and inside-cloud experiments have been performed over eight weeks where each experiment have more then four million QoS measurements. Our availability analysis shows that a remote IoT connected to a less busy cloud region gives higher availability as compared to an IoT device inside a busy cloud region. IIoT and regional cloud services provide good QoS with 99% to 99.9% availability for 1sec soft real-time requirements. In 100ms applications, more efforts are required to achieve higher then 95% availability and design industrial SLA. IIoT applications with 10sec latency like machine learning models can get 99.9% availability with cloud. Availability loss due to communication is almost 1% for 100ms applications. These results also provide requirements and future work of industrial edge computing for IIoT on real-time cloud.

Place, publisher, year, edition, pages
2019. p. 1403-1406
Keywords [en]
cloud computing, Internet of Things, learning (artificial intelligence), production engineering computing, quality of service, IoT device, service-oriented cloud, industrial IoT applications, fourth industrial revolution, service level agreement, SLA availability, end-to-end QoS, IIoT architecture, device-to-cloud, cloud-to-cloud, QoS measurements, machine learning, Computer architecture, Real-time systems, Logic gates, Performance evaluation, Industrial Internet of Things, Service Oriented, Availability
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-42567DOI: 10.1109/ETFA.2019.8869274Scopus ID: 2-s2.0-85074207776OAI: oai:DiVA.org:ri-42567DiVA, id: diva2:1384761
Conference
2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-02-04Bibliographically approved

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Sandström, KristianEricsson, Niclas

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