Abstract
This paper presents an evolutionary approach for multi-objective performance optimization of Self-Adaptive Systems, represented by a specific family of Queuing Network models, namely SMAPEA QNs. The approach is based on NSGA-II genetic algorithm and it is aimed at suggesting near-optimal alternative architectures in terms of mean response times for the different available system operational modes. The evaluation is performed through a controlled experiment with respect to a realistic case study, with the aim of establishing whether meta-heuristics are worth to be investigated as a valid support to performance optimization of Self-Adaptive Systems.
Supported by the Italian Ministry of Education, University and Research – MIUR, L. 297, art. 10.
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Notes
- 1.
The two factors 0.812 and 1.222 have been respectively obtained by solving the equations: \(rt' + 0.1 \times rt' = rt - 0.1 \times rt\) and \(rt' - 0.1 \times rt' = rt + 0.1 \times rt\), as the adopted simulation confidence interval is \(\pm 10\%\) (0.1).
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Arcelli, D. (2020). A Multi-objective Performance Optimization Approach for Self-adaptive Architectures. In: Jansen, A., Malavolta, I., Muccini, H., Ozkaya, I., Zimmermann, O. (eds) Software Architecture. ECSA 2020. Lecture Notes in Computer Science(), vol 12292. Springer, Cham. https://doi.org/10.1007/978-3-030-58923-3_9
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