Meeting Smart City Latency Demands with SDN

Holík, Filip

doi:10.1007/978-3-030-14132-5_4

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…The industry poses distinct performance requirements on IoT applications. For instance, the latency requirement in industrial automation is much demanding, e.g., <2 ms cycle time for motion control, 2-10 ms for factory automation, ∼50 ms for process monitoring and 10-100 ms for video-operated remote control [54] while latency is relatively tolerable in the massive IoT category, e.g., 40-500 ms for traffic management, <1 s for audio and video transfer in smart grid [55], etc. The payload in industrial communication can range from a few bytes for process automation up to 250 bytes for machine control [54] and 1.5 kilobytes for smart grid [56].…”

Section: Highlights Of Experimental Resultsmentioning

confidence: 99%

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Liu

Lan

Pang

et al. 2021

IEEE Open J. Ind. Electron. Soc.

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Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance.

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Section: Highlights Of Experimental Resultsmentioning

confidence: 99%

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Liu

Lan

Pang

et al. 2021

IEEE Open J. Ind. Electron. Soc.

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show abstract

“…Some quantitative requirements of IoT applications can be found in the literature. For instance, the latency requirement in industrial automation is much demanding, e.g., <2 ms cycle time for motion control, 2-10 ms for factory automation, ∼50 ms for process monitoring and 10-100 ms for videooperated remote control [7] while latency is relatively tolerable in the massive IoT category, e.g., 40-500 ms for traffic management, <1 s for audio and video transfer in smart grid [8], etc. The payload in industrial communication can range from a few bytes for process automation up to 250 bytes for machine control [7] and 1.5 kilobytes for smart grid [9].…”

Section: Iot Segmentationmentioning

confidence: 99%

Enable the landing of Internet of Things: a holistic approach

Liu

2021

Linköping Studies in Science and Technology. Dissertations

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for their valuable input and support to my study, research, and life. Thanks to Associate Professor Vangelis Angelakis who showed me another methodology to conduct research. Special thanks to Professor Michael Hörnquist who genuinely helped me a lot to stay and to develop.Moreover, I would like to show my appreciation to my colleagues and partners within the projects I was involved, Ola Weister, Kahin Akram Hasv san, Professor Anita Lloyd Spetz, Docent Donatella Puglisi, Tomas Uusitalo, Åke Johansson, and Patrik Arven, for their trust and support. I sincerely thank the company of my dear colleagues and friends from the Chinese students and scholars association and in my life. I do enjoy the friendship and fun we had.Vinnova, Swedish Environmental Protection Agency, and Norrköping Fund for Research and Development in Sweden are acknowledged for financial support of the Ph.D. study.Last but not least, I want to express my deepest gratitude to my family, my wife Xinchen Wang, my parents and my parents in law. Without your firm support behind me, I could not have gone so far.

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Development of Artificial Intelligence Based Module to Industrial Network Protection System

Holík

Doležel

Merta

et al. 2020

Advances in Intelligent Systems and Computing

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The paper deals with the software-defined networking concept applied to industrial networks. This innovative concept supports network programmability and dynamic implementation of customized features, including security related ones. In a previous work of the authors, the industrial network protection system (INPS) was designed and implemented. The INPS provides complex security features of various traditional and modern security solutions within a single system. In this paper, the AI module, which is one of the crucial parts of the INPS, is dealt with. In particular, a detailed report focused on the development of the AI module decision function is provided. As a result, an artificial neural network, used for the network traffic evaluation in the AI module, is developed and comprehensively tested.

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Meeting Smart City Latency Demands with SDN

Cited by 6 publications

References 8 publications

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Enable the landing of Internet of Things: a holistic approach

Development of Artificial Intelligence Based Module to Industrial Network Protection System

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