Language Abstractions and Techniques for Developing Collective Adaptive Systems Using Context-oriented Programming

Collective Adaptive Systems (CAS) are increasingly used as a model for building and managing largescale cyber-physical systems, highlighting the need for a flexible approach for their definition and engineering. CAS consist of multiple individual context-aware self-adaptive systems, which need to ad...

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Veröffentlicht in:2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) S. 133 - 138
Hauptverfasser: Cardozo, Nicolas, Dusparic, Ivana
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2020
Schlagworte:
Adaptation models
Adaptive systems
Context
Context modeling
Engines
Monitoring
Programming
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Zusammenfassung:Collective Adaptive Systems (CAS) are increasingly used as a model for building and managing largescale cyber-physical systems, highlighting the need for a flexible approach for their definition and engineering. CAS consist of multiple individual context-aware self-adaptive systems, which need to adapt to their own environment, but also interact with each other, resulting in the global CAS behavior. Interactions can emerge as complementary or conflicting, and needs to be managed to assure a correct global behavior. Context-oriented Programming (COP) is one of the programming paradigms that has risen as a suitable approach to developing self-adaptive systems, due to the natural mapping of its programming constructs to self-adaptive systems taxonomy. Recently, multiple extensions to the COP model have been developed, which manage interactions between adaptive components (or systems), enabling its suitability for developing CAS as well. In this paper, we map out the COP language abstractions to the characteristics of CAS, demonstrating COP applicability, its formalization, verification tools, and adaptation composition techniques to foster CAS. Our discussion is illustrated using city-wide bus transport management system, as an example of a CAS.
DOI:10.1109/ACSOS-C51401.2020.00044