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Coordinated actors for reliable self-adaptive systems

Bagheri, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-319-57666-4_15
  3. Abstract:
  4. Self-adaptive systems are systems that automatically adapt in response to environmental and internal changes, such as possible failures and variations in resource availability. Such systems are often realized by a MAPE-K feedback loop, where Monitor, Analyze, Plan and Execute components have access to a runtime model of the system and environment which is kept in the Knowledge component. In order to provide guarantees on the correctness of a self-adaptive system at runtime, the MAPE-K feedback loop needs to be extended with assurance techniques. To address this issue, we propose a coordinated actor-based approach to build a reusable and scalable model@runtime for self-adaptive systems in the domain of track-based traffic control systems. We demonstrate the approach by implementing an automated Air Traffic Control system (ATC) using Ptolemy tool.We compare different adaptation policies on the ATC model based on performance metrics and analyze combination of policies in different configurations of the model. We enriched our framework with runtime performance analysis such that for any unexpected change, subsequent behavior of the model is predicted and results are used for adaptation at the change-point. Moreover, the developed framework enables checking safety properties at runtime. © Springer International Publishing AG 2017
  5. Keywords:
  6. Air traffic control system ; Adaptive systems ; Air navigation ; Air traffic control ; Computer software ; Control systems ; Control towers ; Cyber physical system ; Embedded systems ; Feedback ; Traffic control ; Adaptation policies ; Knowledge components ; Model@Runtime ; Performance analysis ; Performance metrics ; Resource availability ; Run-time performance ; Self-adaptive system ; Adaptive control systems
  7. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 19 October 2016 through 21 October 2016 ; Volume 10231 LNCS , 2017 , Pages 241-259 ; 03029743 (ISSN) ; 9783319576657 (ISBN)
  8. URL: https://link.springer.com/chapter/10.1007/978-3-319-57666-4_15