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Machine-learning abstractions for component-based self-optimizing systems

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  • Special Section: Rigorous Engineering of Collective Adaptive Systems
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Abstract

This paper features an approach that combines machine-learning abstractions with a component model. We target modern self-optimizing systems and therefore integrate the machine-learning abstractions into our ensemble-based component model DEECo. We further endow the DEECo component model with abstractions for specifying self-optimization heuristics, which address coordination among multiple components. We demonstrate these abstractions in the context of an Industry 4.0 use case. We argue that incorporating machine learning and optimization heuristics is the key feature for modern smart systems, which learn over time and optimize their behavior at runtime to deal with uncertainty in their environment.

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Funding

This work has been partially supported by Charles University institutional funding SVV 260698/2023, partially supported by the Czech Science Foundation project 20-24814J, partially supported by the Charles University Grant Agency project 269723, and partially supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 810115).

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Authors and Affiliations

  1. Charles University, Prague, Czech Republic

    Michal Töpfer, Milad Abdullah, Tomáš Bureš, Petr Hnětynka & Martin Kruliš

Authors
  1. Michal Töpfer
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  2. Milad Abdullah
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  3. Tomáš Bureš
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  4. Petr Hnětynka
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  5. Martin Kruliš
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Correspondence to Michal Töpfer.

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Töpfer, M., Abdullah, M., Bureš, T. et al. Machine-learning abstractions for component-based self-optimizing systems. Int J Softw Tools Technol Transfer 25, 717–731 (2023). https://doi.org/10.1007/s10009-023-00726-x

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