A Multi-objective Performance Optimization Approach for Self-adaptive Architectures
| Autor: | Davide Arcelli |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Computer science Software architecture Search-based software engineering Mean and predicted response 020207 software engineering Self adaptive 02 engineering and technology Genetic algorithms Multi-objective optimization Queuing networks Self-adaptive systems Software performance engineering Queuing network model Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Controlled experiment |
| Zdroj: | Software Architecture ISBN: 9783030589226 ECSA |
| DOI: | 10.1007/978-3-030-58923-3_9 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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