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A distributed formal-based model for self-healing behaviors in autonomous systems: from failure detection to self-recovery.

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Title: A distributed formal-based model for self-healing behaviors in autonomous systems: from failure detection to self-recovery.
Authors: Hafaiedh, Imene Ben1 (AUTHOR) imen.benhafaiedh@isi.utm.tn, Slimane, Maroua Ben2 (AUTHOR)
Source: Journal of Supercomputing. Nov2022, Vol. 78 Issue 17, p18725-18753. 29p.
Subjects: System failures, Autonomic computing, Computer systems, Fault tolerance (Engineering), Self-adaptive software, Behavioral assessment, Heterogeneous computing
Abstract: The challenges of current software-intensive systems, large-scale information and computing systems environments, which are highly dynamic, heterogeneous, and unpredictable, have motivated the development of techniques that enhance these systems with autonomous behaviors. Even though different concerns about these systems have been deeply studied, their design is still considerably more challenging than traditional ones. Self-healing is one of the main features that characterize autonomic computing systems. Failure detection, recovery strategies, and reliability are of paramount importance to ensure continuous operation and correct functioning even in the presence of a given maximum amount of faulty components. Most existing research and implementations focus on architecture-specific solutions to introduce self-healing behaviors. This implies that users must tailor their software by taking into account architecture-specific fault tolerance features, which requires too much effort from developers and users. This paper proposes a distributed formal model for the specification, verification, and analysis of self-healing behaviors in autonomous systems, from failure-detection to self-recovery. Such a high-level model allows users to specify and apply the desired type of failure detection and recovery without requiring any knowledge about its implementation. Our model allows not only formal verification of different properties but also performance evaluation. We provide the verification of qualitative properties using state-space exploration tools, and quantitative properties are also validated through statistical model-checking. All these properties are preserved in actual implementation by ensuring that the deployed code is consistent with the validated model. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Supercomputing is the property of Springer Nature 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: <searchLink fieldCode="DE" term="%22System+failures%22">System failures</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomic+computing%22">Autonomic computing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+tolerance+%28Engineering%29%22">Fault tolerance (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Self-adaptive+software%22">Self-adaptive software</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneous+computing%22">Heterogeneous computing</searchLink>
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  Data: The challenges of current software-intensive systems, large-scale information and computing systems environments, which are highly dynamic, heterogeneous, and unpredictable, have motivated the development of techniques that enhance these systems with autonomous behaviors. Even though different concerns about these systems have been deeply studied, their design is still considerably more challenging than traditional ones. Self-healing is one of the main features that characterize autonomic computing systems. Failure detection, recovery strategies, and reliability are of paramount importance to ensure continuous operation and correct functioning even in the presence of a given maximum amount of faulty components. Most existing research and implementations focus on architecture-specific solutions to introduce self-healing behaviors. This implies that users must tailor their software by taking into account architecture-specific fault tolerance features, which requires too much effort from developers and users. This paper proposes a distributed formal model for the specification, verification, and analysis of self-healing behaviors in autonomous systems, from failure-detection to self-recovery. Such a high-level model allows users to specify and apply the desired type of failure detection and recovery without requiring any knowledge about its implementation. Our model allows not only formal verification of different properties but also performance evaluation. We provide the verification of qualitative properties using state-space exploration tools, and quantitative properties are also validated through statistical model-checking. All these properties are preserved in actual implementation by ensuring that the deployed code is consistent with the validated model. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature 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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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s11227-022-04614-0
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      – Code: eng
        Text: English
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        PageCount: 29
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      – SubjectFull: System failures
        Type: general
      – SubjectFull: Autonomic computing
        Type: general
      – SubjectFull: Computer systems
        Type: general
      – SubjectFull: Fault tolerance (Engineering)
        Type: general
      – SubjectFull: Self-adaptive software
        Type: general
      – SubjectFull: Behavioral assessment
        Type: general
      – SubjectFull: Heterogeneous computing
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      – TitleFull: A distributed formal-based model for self-healing behaviors in autonomous systems: from failure detection to self-recovery.
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            NameFull: Hafaiedh, Imene Ben
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            NameFull: Slimane, Maroua Ben
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            – D: 15
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              Text: Nov2022
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              Y: 2022
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