Machine-learning-based self-adaptation of component ensembles
| Název práce v češtině: | Adaptace komponent a jejich kooperace na základě strojového učení |
|---|---|
| Název v anglickém jazyce: | Machine-learning-based self-adaptation of component ensembles |
| Klíčová slova: | adaptace|strojové učení|komponenty|kooperace |
| Klíčová slova anglicky: | self-adaptive|machine learning|components|ensembles |
| Akademický rok vypsání: | 2021/2022 |
| Typ práce: | diplomová práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra distribuovaných a spolehlivých systémů (32-KDSS) |
| Vedoucí / školitel: | prof. RNDr. Tomáš Bureš, Ph.D. |
| Řešitel: | RNDr. Michal Töpfer - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 09.11.2021 |
| Datum zadání: | 09.11.2021 |
| Datum potvrzení stud. oddělením: | 04.01.2022 |
| Datum a čas obhajoby: | 15.06.2022 09:00 |
| Datum odevzdání elektronické podoby: | 03.05.2022 |
| Datum odevzdání tištěné podoby: | 16.05.2022 |
| Datum proběhlé obhajoby: | 15.06.2022 |
| Oponenti: | doc. RNDr. Pavel Parízek, Ph.D. |
| Zásady pro vypracování |
| Modern smart systems, such as IoT (Internet of Things) and CPS (Cyber-Physical Systems) are typically required to dynamically self-adapt and reconfigure their structure based on the situation in the environment. To manage the complexity and describe the dynamically evolving architecture of such systems, architectural models featuring cooperating components have been introduced. One important representative of these approaches are the autonomic component ensembles [1], which ensure the coordination among the components which pursue a same goal. The ensembles are formed and dismantled dynamically based on the changes of the properties of the components and they can overlap and be nested. The formation of the ensembles was originally formulated as a constraint optimization problem. Later, a machine-learning-based solution was proposed to improve the ensemble resolution [2].
The goal of this thesis is to further integrate the machine learning algorithms with the ensemble resolution process. In particular, the thesis will focus on how machine learning can be used to predict values that the self-adaptive systems base their adaptation decisions on. This will allow proactive self-adaptation based on architectural description with ensembles. The predictions should be transparent for the ensemble resolution process so the predicted properties can be used for the resolution as if they were normal properties of the component. Further, the approach shall be aligned with the architectural perspective of autonomic component ensembles. |
| Seznam odborné literatury |
| [1] Bures, T., Gerostathopoulos, I., Hnetynka, P. et al. A language and framework for dynamic component ensembles in smart systems. Int J Softw Tools Technol Transfer 22, 497–509 (2020). https://doi.org/10.1007/s10009-020-00558-z
[2] Bureš T., Gerostathopoulos I., Hnětynka P., Pacovský J. (2020) Forming Ensembles at Runtime: A Machine Learning Approach. In: Margaria T., Steffen B. (eds) Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles. ISoLA 2020. Lecture Notes in Computer Science, vol 12477. Springer, Cham. https://doi.org/10.1007/978-3-030-61470-6_26 |