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FastReact: In-Network Control And Caching For Industrial Control Networks Using Programmable Data Planes
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-7358-8675
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-9446-8143
ABB Corp Res, Vasteras.
2018 (English)In: 2018 IEEE 23RD INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2018, p. 219-226Conference paper, Published paper (Refereed)
Abstract [en]

Providing network reliability as well as low and predictable latency is important especially for Industrial Automation and Control Networks. However, diagnosing link status from the control plane has high latency and overhead. In addition, the communication with the industrial controller may impose additional network latency. We present FastReact - a system enabling In-Network monitoring, control and caching for Industrial Automation and Control Networks. FastReact outsources simple monitoring and control actions to evolving programmable data planes using the P4 language. As instructed by the Industrial Controller through a Northbound API, the SDN controller composes control actions using Boolean Logic which are then installed in the data plane. The data plane parses and caches sensor values and performs simple calculations on them which are connected to fast control actions that are executed locally. For resiliency, FastReact monitors liveness and response of sensors/actuators and performs a fast local link repair in the data plane if a link failure is detected. Our testbed measurement show that FastReact can reduce the sensor/actuator delay while being resilient against several failure events.

Place, publisher, year, edition, pages
2018. p. 219-226
Series
IEEE International Conference on Emerging Technologies and Factory Automation-ETFA, ISSN 1946-0740
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-70292ISI: 000449334500026ISBN: 978-1-5386-7108-5 (print)OAI: oai:DiVA.org:kau-70292DiVA, id: diva2:1265499
Conference
23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), SEP 04-07, 2018, Politecnico Torino, Torino, ITALY
Available from: 2018-11-23 Created: 2018-11-23 Last updated: 2019-01-31Bibliographically approved

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Vestin, JonathanKassler, Andreas

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  • apa
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