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EnglishThis 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.
| Titolo: | A multi-objective performance optimization approach for self-adaptive architectures | |
| Autori: | ARCELLI, DAVIDE (Corresponding) | |
| Data di pubblicazione: | 2020 | |
| Serie: | ||
| Handle: | http://hdl.handle.net/11697/150249 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11697/150249
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