A Multi-objective Performance Optimization Approach for Self-adaptive Architectures

被引：2
|
作者：
Arcelli, Davide [1 ]
机构：
[1] Univ Aquila, Via Vetoio 1, Laquila, Italy
来源：
SOFTWARE ARCHITECTURE (ECSA 2020) | 2020年 / 12292卷
关键词：
Self-adaptive systems; Software architecture; Software performance engineering; Search-based software engineering; Multi-objective optimization; Genetic algorithms; Queuing networks;
D O I：
10.1007/978-3-030-58923-3_9
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：139 / 147
页数：9
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