Machine learning techniques in self-adaptive systems
| Thesis title in Czech: | Techniky strojového učení v self-adaptivních systémech |
|---|---|
| Thesis title in English: | Machine learning techniques in self-adaptive systems |
| Academic year of topic announcement: | 2021/2022 |
| Thesis type: | dissertation |
| Thesis language: | angličtina |
| Department: | Department of Distributed and Dependable Systems (32-KDSS) |
| Supervisor: | prof. RNDr. Tomáš Bureš, Ph.D. |
| Author: | Mgr. Michal Töpfer - assigned and confirmed by the Study Dept. |
| Date of registration: | 02.08.2022 |
| Date of assignment: | 02.08.2022 |
| Confirmed by Study dept. on: | 04.10.2022 |
| Guidelines |
| 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. |
| References |
| [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 |