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Self-healing Systems Monitoring

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Advances in Self-healing Systems Monitoring and Data Processing

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 425))

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Abstract

Monitoring the condition of Self-healing Systems is a compulsory system component. The authors proposed an approach to identifying anomalies in ShS operation based on machine learning technology. The proposed architecture of the monitoring system using autonomous software agents. The architecture provides for the dynamic development of a hierarchical structure, the node of which can be any entity that is determined by the data source or sensor. For interaction among all agents, it is proposed to use a group of intelligent query agents whose purpose is to coordinate information gathering agents, restructure the received information and implement protocols and messaging mechanisms among all agents of the model. In the context of ShS monitoring, there may exist the metrics of grids, clusters, computational nodes and tasks, and so on. Based on this approach, a methodology for monitoring the system condition is proposed. The proposed methodology determines the conditions and the procedure for assessing the ShS condition using the developed multi-agent monitoring system.

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Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, 14 Nauki ave, Kharkiv, 61166, Ukraine

    Igor Ruban, Vitalii Martovytskyy & Olesia Barkovska

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  1. Igor Ruban
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  2. Vitalii Martovytskyy
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  3. Olesia Barkovska
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Editors and Affiliations

  1. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Igor Ruban

  2. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Andriy Kovalenko

  3. Faculty of Management Science and Informatics, University of Žilina, Zilina, Slovakia

    Vitaly Levashenko

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Ruban, I., Martovytskyy, V., Barkovska, O. (2022). Self-healing Systems Monitoring. In: Ruban, I., Kovalenko, A., Levashenko, V. (eds) Advances in Self-healing Systems Monitoring and Data Processing. Studies in Systems, Decision and Control, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-030-96546-4_1

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