Machine learning techniques in self-adaptive systems
| Název práce v češtině: | Techniky strojového učení v self-adaptivních systémech |
|---|---|
| Název v anglickém jazyce: | Machine learning techniques in self-adaptive systems |
| Akademický rok vypsání: | 2021/2022 |
| Typ práce: | disertační práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra distribuovaných a spolehlivých systémů (32-KDSS) |
| Vedoucí / školitel: | prof. RNDr. Tomáš Bureš, Ph.D. |
| Řešitel: | Mgr. Michal Töpfer - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 02.08.2022 |
| Datum zadání: | 02.08.2022 |
| Datum potvrzení stud. oddělením: | 04.10.2022 |
| Zásady pro vypracování |
| Machine learning has become a significant trend in self-adaptive systems [1], [2], [3], [4]. It is often used to infer hidden and future state of the system [1] to allow the self-adaptation mechanism to take proactive adaptation steps. Similarly, machine learning is sometimes used to decide on the cost and benefits of adaptation actions and thus help select the best adaptation action [5].
The goal of this thesis is to explore machine learning options in the context of self-adaptive systems and to propose systematic model-based approach to introduce machine learning in design of self-adaptive systems. |
| Seznam odborné literatury |
| [1] H. Muccini and K. Vaidhyanathan, “A machine learning-driven approach for proactive decision making in adaptive architectures,” in Companion Proceedings of ICSA 2019, Hamburg, Germany, 2019, pp. 242–245.
[2] O. Gheibi, D. Weyns, and F. Quin, “Applying Machine Learning in Self- adaptive Systems: A Systematic Literature Review,” ACM Transactions on Autonomous and Adaptive Systems, vol. 15, no. 3, pp. 9:1–9:37, Aug. 2021. [3] T. R. D. Saputri and S.-W. Lee, “The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review,” IEEE Access, vol. 8, pp. 205 948–205 967, 2020. [4] D. Weyns, B. Schmerl, M. Kishida, A. Leva, M. Litoiu, N. Ozay, C. Paterson, and K. Tei, “Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning,” in Proceedings of SEAMS 2021, Madrid, Spain. IEEE, May 2021, pp. 217–223. [6] J. Van Der Donckt, D. Weyns, M. U. Iftikhar, and S. S. Buttar, “Effective Decision Making in Self-adaptive Systems Using Cost-Benefit Analysis at Runtime and Online Learning of Adaptation Spaces,” in Evaluation of Novel Approaches to Software Engineering, ser. LNCS. Springer, 2019, vol. 1023, pp. 373–403 |