Attuning Adaptation Rules via a Rule-Specific Neural Network

Author:

Bureš Tomáš,Hnětynka Petr,Kruliš Martin,Plášil František,Khalyeyev Danylo,Hahner Sebastian,Seifermann Stephan,Walter Maximilian,Heinrich Robert

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Paper results replicaton package. https://github.com/smartarch/attuning-adaptation-rules-replication-package

2. Anaya, I.D.P., Simko, V., Bourcier, J., Plouzeau, N., Jézéquel, J.M.: A prediction-driven adaptation approach for self-adaptive sensor networks. In: Proceedings of SEAMS 2014, Hyderabad, India (2014)

3. Bierzynski, K., Lutskov, P., Assmann, U.: Supporting the self-learning of systems at the network edge with microservices. In: 13th International Conference and Exhibition on Smart Systems Integration Issues of Miniaturized Systems (2019)

4. Bureš, T., Gerostathopoulos, I., Hnětynka, P., Pacovský, J.: Forming ensembles at runtime: a machine learning approach. In: Proceedings of ISOLA 2020, Rhodes, Greece (2020)

5. Chen, T., Bahsoon, R.: Self-adaptive and online QoS modeling for cloud-based software services. IEEE Trans. Softw. Eng. 43(5), 453–475 (2017)

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1. Generating adaptation rule-specific neural networks;International Journal on Software Tools for Technology Transfer;2023-11-07

2. Rigorous Engineering of Collective Adaptive Systems Introduction to the 4th Track Edition;Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning;2022

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