From Brown-Field to Future Industrial Networks, a Case Study

Author:

Lavassani MehrzadORCID,Åkerberg Johan,Björkman Mats

Abstract

The network infrastructures in the future industrial networks need to accommodate, manage and guarantee performance to meet the converged Internet technology (IT) and operational technology (OT) traffics requirements. The pace of IT–OT networks development has been slow despite their considered benefits in optimizing the performance and enhancing information flows. The hindering factors vary from general challenges in performance management of the diverse traffic for green-field configuration to lack of outlines for evolving from brown-fields to the converged network. Focusing on the brown-field, this study provides additional insight into a brown-field characteristic to set a baseline that enables the subsequent step development towards the future’s expected converged networks. The case study highlights differences between real-world network behavior and the common assumptions for analyzing the network traffic covered in the literature. Considering the unsatisfactory performance of the existing methods for characterization of brown-field traffic, a performance and dynamics mixture measurement is proposed. The proposed method takes both IT and OT traffic into consideration and reduces the complexity, and consequently improves the flexibility, of performance and configuration management of the brown-field.

Funder

PiiA

VINNOVA

Svenska Forskningsrådet Formas

Energimyndigheten

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference22 articles.

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Federated Learning for Network Anomaly Detection in a Distributed Industrial Environment;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15

2. Protocol study and anomaly detection for server-driven traffic in SCADA networks;International Journal of Critical Infrastructure Protection;2023-09

3. Data-driven Method for In-band Network Telemetry Monitoring of Aggregated Traffic;2022 IEEE 21st International Symposium on Network Computing and Applications (NCA);2022-12-14

4. A Generative Approach for Production-Aware Industrial Network Traffic Modeling;2022 IEEE Globecom Workshops (GC Wkshps);2022-12-04

5. Modeling and Profiling of Aggregated Industrial Network Traffic;Applied Sciences;2022-01-11

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