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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), IEEE, 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
IEEE, 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: 2019-12-12Bibliographically approved
In thesis
1. SDN-Enabled Resiliency, Monitoring and Control in Computer Networks
Open this publication in new window or tab >>SDN-Enabled Resiliency, Monitoring and Control in Computer Networks
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Next generation networks aim to increase network convergence by allowing a single network architecture to serve diverse traffic types ranging from high-bandwidth video streaming to low-latency industrial automation, while meeting their respective service level requirements. Such a converged network architecture puts high requirements on flexibility, interoperability, and resilience. While current networks exhibit some degree of network convergence, they may not reach the level of interoperability required for future application areas. This is particularly prevalent in networks that depend heavily on closed and proprietary equipment, such as industrial automation and small cell backhaul networks. Recently, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have been proposed as solutions for increased network flexibility. By separating and logically centralizing the network control plane, SDN allows for dynamic control of the network infrastructure. NFV, on the other hand, enables flexibility and scalability through the virtualization and orchestration of network functions.

In this thesis, we investigate how SDN and NFV can be used to make next generation networks more reliable, flexible and programmable. We focus mainly on three areas: resiliency, monitoring, and control. First, we look at the usage of SDN to enable in-network resiliency in wireless access, backhaul and industrial automation networks. Next, we design and evaluate FastReact, a switch program that allows industrial automation networks to partially offload their distributed application logic to the data plane, reducing end to end latency and increasing network resiliency. Finally, we propose combining FastReact control with in-network telemetry event detection, significantly increasing the monitoring capacity by selectively discarding redundant telemetry information in the data plane.

Abstract [en]

Next generation computer networks aim to provide a single network architecture, which can support any type of service, ranging from high-bandwidth video streaming to low-latency industrial automation. Those services have a wide range of network requirements that must be supported by a single converged network, which puts high requirements on flexibility, interoperability, and resilience.

Recently, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have been proposed as solutions for increased network flexibility. By separating and logically centralizing the network control plane, SDN allows for dynamic control of the network infrastructure. NFV, on the other hand, enables flexibility and scalability through the virtualization and orchestration of network functions.

In this thesis, we investigate how SDN and NFV can be used to make next generation networks more reliable, flexible and programmable. We focus mainly on three different areas: resiliency, monitoring, and control, and how they can be improved upon through using SDN. 

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2020. p. 39
Series
Karlstad University Studies, ISSN 1403-8099 ; 2020:2
Keywords
software defined networking, data plane programming, wireless, industrial automation, in-network telemetry, complex event processing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-75953 (URN)978-91-7867-074-1 (ISBN)978-91-7867-075-8 (ISBN)
Public defence
2020-02-04, 1B306, Fryxellsalen, Universitetsgatan 1, KARLSTAD, 10:15 (English)
Opponent
Supervisors
Available from: 2020-01-13 Created: 2019-12-12 Last updated: 2020-02-03Bibliographically approved

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Vestin, JonathanKassler, Andreas

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