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SCADA Systems in the Cloud

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Handbook of Big Data Technologies

Abstract

SCADA (Supervisory Control And Data Acquisition) systems allow users to monitor (using sensors) and control (using actuators) an industrial system remotely. Larger SCADA systems can support several 100,000 sensors, sending and storing hundreds of thousands of messages per second, generating large amounts of data. As these systems are critical to industrial processes, they are often run on highly reliable and dedicated hardware. This is in contrast to the current state of computing, which is moving from running applications on internally hosted servers to cheaper, internal or external cloud environments. Clouds can benefit SCADA users by providing the storage and processing power to analyse the collected data. The goal of this chapter is twofold; provide an introduction to techniques for migrating SCADA to clouds, and devise a conceptual system which supports the process of migrating a SCADA application to a cloud resource while fulfilling key SCADA requirements (such as; support for big data storage).

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Authors and Affiliations

  1. School of CS and IT, RMIT University, Melbourne, Australia

    Philip Church, Caspar Ryan, Andrzej Goscinski & Zahir Tari

  2. Corporate Technology, Siemens AG, Munich, Germany

    Harald Mueller, Spyridon V. Gogouvitis & Houssam Haitof

  3. School of IT, Deakin University, Geelong, Waurn Ponds, Australia

    Philip Church & Andrzej Goscinski

Authors
  1. Philip Church
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  2. Harald Mueller
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  3. Caspar Ryan
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  4. Spyridon V. Gogouvitis
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  5. Andrzej Goscinski
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  6. Houssam Haitof
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  7. Zahir Tari
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Correspondence to Zahir Tari .

Editor information

Editors and Affiliations

  1. School of Information Technologies, The University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  2. The School of Computer Science, The University of New South Wales, Eveleigh, New South Wales, Australia

    Sherif Sakr

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Church, P. et al. (2017). SCADA Systems in the Cloud. In: Zomaya, A., Sakr, S. (eds) Handbook of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-49340-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-49340-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49339-8

  • Online ISBN: 978-3-319-49340-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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