Abstract
Smart self-adaptive systems are nowadays commonly employed almost in any application domain. Within them, groups of robots, autonomous vehicles, drones, and similar automatons dynamically cooperate to achieve a common goal. An approach to model such dynamic cooperation is via autonomic component ensembles, which are dynamically formed groups of components. Forming ensembles is described via a set of constraints (e.g., form an ensemble of three drones closest to a target that have sufficient battery level to reach the target and stay there). Evaluating these constraints by traditional means (such as a SAT solver) is computationally demanding and does not scale for large systems. This paper proposes an approach for solving ensemble formations based on machine learning which may be relatively faster. The method trains the model on a small instance of the system governed by a computationally demanding algorithm and then adapts it for large instances thanks to the generalization properties of the machine learning model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
jRESP: Java Runtime Environment for SCEL Programs. http://jresp.sourceforge.net/. Accessed 02 Jan 2023
Alrahman, Y.A., De Nicola, R., Loreti, M.: Programming interactions in collective adaptive systems by relying on attribute-based communication. Sci. Comput. Program. 192, 102428 (2020). https://doi.org/10.1016/j.scico.2020.102428
Bureš, T., Gerostathopoulos, I., Hnětynka, P., Pacovský, J.: Forming ensembles at runtime: a machine learning approach. In: Margaria, T., Steffen, B. (eds.) ISoLA 2020. LNCS, vol. 12477, pp. 440–456. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61470-6_26
Bures, T., et al.: A language and framework for dynamic component ensembles in smart systems. Int. J. Softw. Tools Technol. Transfer 22(4), 497–509 (2020). https://doi.org/10.1007/s10009-020-00558-z
Cámara, J., Muccini, H., Vaidhyanathan, K.: Quantitative verification-aided machine learning: a tandem approach for architecting self-adaptive IoT systems. In: Proceedings of ICSA 2021, Salvador, Brazil, pp. 11–22. IEEE, March 2020. https://doi.org/10.1109/ICSA47634.2020.00010
De Nicola, R., Duong, T., Loreti, M.: ABEL - a domain specific framework for programming with attribute-based communication. In: Riis Nielson, H., Tuosto, E. (eds.) COORDINATION 2019. LNCS, vol. 11533, pp. 111–128. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22397-7_7
Gabor, T., et al.: The scenario coevolution paradigm: adaptive quality assurance for adaptive systems. Int. J. Softw. Tools Technol. Transfer 22(4), 457–476 (2020). https://doi.org/10.1007/s10009-020-00560-5
Gheibi, O., Weyns, D., Quin, F.: Applying machine learning in self-adaptive systems: a systematic literature review. ACM Trans. Auton. Adapt. Syst. 15(3), 9:1–9:37 (2021). https://doi.org/10.1145/3469440
Gheibi, O., Weyns, D., Quin, F.: On the impact of applying machine learning in the decision-making of self-adaptive systems. In: Proceedings of SEAMS 2021, Madrid, Spain, pp. 104–110. IEEE, May 2021. https://doi.org/10.1109/SEAMS51251.2021.00023
Bjørner, D.: Domain endurants. In: Iida, S., Meseguer, J., Ogata, K. (eds.) Specification, Algebra, and Software. LNCS, vol. 8373, pp. 1–34. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54624-2_1
Muccini, H., Vaidhyanathan, K.: A machine learning-driven approach for proactive decision making in adaptive architectures. In: Companion Proceedings of ICSA 2019, Hamburg, Germany, pp. 242–245 (2019). https://doi.org/10.1109/ICSA-C.2019.00050
De Nicola, R., et al.: The SCEL language: design, implementation, verification. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 3–71. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16310-9_1
Saputri, T.R.D., Lee, S.W.: The application of machine learning in self-adaptive systems: a systematic literature review. IEEE Access 8, 205948–205967 (2020). https://doi.org/10.1109/ACCESS.2020.3036037
Van Der Donckt, J., Weyns, D., Iftikhar, M.U., Buttar, S.S.: Effective decision making in self-adaptive systems using cost-benefit analysis at runtime and online learning of adaptation spaces. In: Damiani, E., Spanoudakis, G., Maciaszek, L.A. (eds.) ENASE 2018. CCIS, vol. 1023, pp. 373–403. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22559-9_17
Van Der Donckt, J., Weyns, D., Quin, F., Van Der Donckt, J., Michiels, S.: Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals. In: Proceedings of SEAMS 2020, Seoul, South Korea, pp. 20–30. ACM (2020). https://doi.org/10.1145/3387939.3391605
Acknowledgment
This work has been partially supported by the Czech Science Foundation project 20-24814J, and partially by Charles University institutional funding SVV 260698/2023.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pacovský, J., Hnětynka, P., Kruliš, M. (2023). Generalization of Machine-Learning Adaptation in Ensemble-Based Self-adaptive Systems. In: Batista, T., Bureš, T., Raibulet, C., Muccini, H. (eds) Software Architecture. ECSA 2022 Tracks and Workshops. ECSA 2022. Lecture Notes in Computer Science, vol 13928. Springer, Cham. https://doi.org/10.1007/978-3-031-36889-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-36889-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36888-2
Online ISBN: 978-3-031-36889-9
eBook Packages: Computer ScienceComputer Science (R0)