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A Taxonomical Framework for Autonomic Computing Within Cyber-Physical Systems of Systems.

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Title: A Taxonomical Framework for Autonomic Computing Within Cyber-Physical Systems of Systems.
Authors: Berezovskyi, Andrii1 (AUTHOR) andriib@kth.se, Mokrushin, Leonid2 (AUTHOR), Asplund, Fredrik1 (AUTHOR), El-khoury, Jad1 (AUTHOR), Inam, Rafia1,2 (AUTHOR), Fersman, Elena2 (AUTHOR)
Source: Journal of Integrated Design & Process Science. Nov2023, Vol. 27 Issue 3/4, p235-247. 13p.
Subjects: Autonomic computing, Cyber physical systems, Automation software, Taxonomy, System of systems, Decentralized control systems
Abstract: An increase in size and complexity prompts research of autonomous control of connected cyber-physical systems (CPS) at the level of intents (high-level goals). Domain-specific solutions such as intent-based networking, and the next generation of supervisory control and data acquisition and manufacturing execution systems, were influenced by the concept of autonomic computing and Monitor-Analyze-Plan-Execute loops over shared knowledge to introduce such control. However, there is a dearth of knowledge concerning the architectural attributes required to enable autonomic computing at a domain-independent level. CPSs are also often contributed by multiple stakeholders, thereby forming systems of systems in which architectures are by necessity federated. Herein, we apply a systematic approach to develop a taxonomy of such attributes, considering the federated nature of architectures, the need to accommodate heterogeneity and the legacy systems commonly encountered in practice. The proposed taxonomy covers nine key dimensions present within different domains of autonomic computing: knowledge representation, learning, distribution, reactivity, environment observability, information acquisition automation, information analysis automation, decision selection automation, and action implementation automation. The utility of the taxonomy is further demonstrated through an application to architectures across domains and scales. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Integrated Design & Process Science is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Taxonomical Framework for Autonomic Computing Within Cyber-Physical Systems of Systems.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Integrated+Design+%26+Process+Science%22">Journal of Integrated Design & Process Science</searchLink>. Nov2023, Vol. 27 Issue 3/4, p235-247. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Autonomic+computing%22">Autonomic computing</searchLink><br /><searchLink fieldCode="DE" term="%22Cyber+physical+systems%22">Cyber physical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Automation+software%22">Automation software</searchLink><br /><searchLink fieldCode="DE" term="%22Taxonomy%22">Taxonomy</searchLink><br /><searchLink fieldCode="DE" term="%22System+of+systems%22">System of systems</searchLink><br /><searchLink fieldCode="DE" term="%22Decentralized+control+systems%22">Decentralized control systems</searchLink>
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  Data: An increase in size and complexity prompts research of autonomous control of connected cyber-physical systems (CPS) at the level of intents (high-level goals). Domain-specific solutions such as intent-based networking, and the next generation of supervisory control and data acquisition and manufacturing execution systems, were influenced by the concept of autonomic computing and Monitor-Analyze-Plan-Execute loops over shared knowledge to introduce such control. However, there is a dearth of knowledge concerning the architectural attributes required to enable autonomic computing at a domain-independent level. CPSs are also often contributed by multiple stakeholders, thereby forming systems of systems in which architectures are by necessity federated. Herein, we apply a systematic approach to develop a taxonomy of such attributes, considering the federated nature of architectures, the need to accommodate heterogeneity and the legacy systems commonly encountered in practice. The proposed taxonomy covers nine key dimensions present within different domains of autonomic computing: knowledge representation, learning, distribution, reactivity, environment observability, information acquisition automation, information analysis automation, decision selection automation, and action implementation automation. The utility of the taxonomy is further demonstrated through an application to architectures across domains and scales. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Integrated Design & Process Science is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Automation software
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              Text: Nov2023
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