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Data-driven Method for In-band Network Telemetry Monitoring of Aggregated Traffic
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Research Institutes of Sweden, Industrial Systems, Sundsvall, Sweden.ORCID iD: 0000-0001-5808-1382
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7159-7508
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-2419-2735
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Under the vision of industry 4.0, industrial networks are expected to accommodate a large amount of aggregated traffic of both operation and information technologies to enable the integration of innovative services and new applications. In this respect, guaranteeing the uninterrupted operation of the installed systems is an indisputable condition for network management. Network measurement and performance monitoring of the underlying communication states can provide invaluable insight for safeguarding the system performance by estimating required and available resources for flexible integration without risking network interruption or degrading network performance. In this work, we propose a data-driven in-band telemetry method to monitor the aggregated traffic of the network at the switch level. The method learns and models the communication states by local network-level measurement of communication intensity. The approximated model parameters provide information for network management for prognostic purposes and congestion avoidance resource planning when integrating new applications. Applying the method also addresses the consequence of telemetry data overhead on QoS since the transmission of telemetry packets can be done based on the current state of the network. The monitoring at the switch level is a step towards the Network-AI for future industrial networks.

Place, publisher, year, edition, pages
2022. p. 89-95
National Category
Communication Systems Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-62049DOI: 10.1109/NCA57778.2022.10013583Scopus ID: 2-s2.0-85147334168ISBN: 9798350397307 (print)OAI: oai:DiVA.org:mdh-62049DiVA, id: diva2:1742722
Conference
21st IEEE International Symposium on Network Computing and Applications, NCA 2022, Virtual, Online, 14 December 2022 through 16 December 2022
Available from: 2023-03-10 Created: 2023-03-10 Last updated: 2023-04-12Bibliographically approved
In thesis
1. Evolving Industrial Networks: Data-Driven Network Traffic Modelling and Monitoring
Open this publication in new window or tab >>Evolving Industrial Networks: Data-Driven Network Traffic Modelling and Monitoring
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The concept of Industrial IoT encompasses the joint applicability of operation and information technologies to expand the efficiency expectation of automation to green and flexible processes with innovative products and services. Future industrial networks need to accommodate, manage and guarantee the performance of converged traffic from different technologies. The network infrastructures are transforming to enable data availability for advanced applications and enhance flexibility. Nonetheless, the pace of IT–OT networks development has been slow despite their considered benefits in optimising performance and enhancing information flows. The hindering factors vary from general challenges in performance management of the diverse traffic for greenfield configuration to the lack of outlines for evolving from brownfield installations without interrupting the operation of ongoing processes. One tangible gap is the lack of insight into the brownfield installation in operation. This dissertation explores the possible evolutionary steps from brownfield installations to future industrial networks.The goal is to ensure the uninterrupted performance of brownfield installations on the path of evolving to the envisioned smart factories. It addresses the gap between the state of the art and state of practice, and the technical prerequisites of the integrated traffic classes for the development of an IIoT monitoring mechanism. A novel lightweight learning algorithm at the sensor level for an IIoT compliance monitoring system, together with a case study of traffic collected from a brownfield installation, provides the baseline of comparative analysis between the common assumptions and the state of practice. The identified gaps and challenges to address them directs the research for proposing a two-step aggregated traffic modelling by introducing new measurement method and performance indicator parameters for capturing the communication dynamics. Lastly, the sensor-level learning algorithm is refined with the knowledge gained from practice and research contributions to propose an in-band telemetry mechanism for monitoring aggregated network traffic.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2023
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 375
National Category
Communication Systems Computer Systems Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-62063 (URN)978-91-7485-588-3 (ISBN)
Public defence
2023-06-13, Lambda, Mälardalens universitet, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2023-03-14 Created: 2023-03-13 Last updated: 2023-05-23Bibliographically approved

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Åkerberg, JohanBjörkman, Mats

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