ECSA 2020 (series) / Research Papers / A Multi-Objective Performance Optimization Approach for Self-Adaptive Architectures
A Multi-Objective Performance Optimization Approach for Self-Adaptive Architecturesshort-paperResearch Track
Fri 18 Sep 2020 16:50 - 17:10 at ECSA 2020 Teams Channel - S13: Self-adaptation and Uncertainity (II) Chair(s): Xabier Larrucea, Gabriel A. Moreno
This paper presents an evolutionary approach for multi-ob-jec-tive 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.
Fri 18 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
Fri 18 Sep
Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:50 - 17:30 | S13: Self-adaptation and Uncertainity (II)Research Papers at ECSA 2020 Teams Channel Chair(s): Xabier Larrucea Tecnalia, Gabriel A. Moreno Carnegie Mellon University Virtualization support: Claudio Di Sipio | ||
16:50 20m | A Multi-Objective Performance Optimization Approach for Self-Adaptive Architecturesshort-paperResearch Track Research Papers Davide Arcelli Università degli Studi dell'Aquila | ||
17:10 20m | Towards Using Probabilistic Models to Design Software Systems with Inherent Uncertaintyshort-paperResearch Track Research Papers Alex Serban Radboud University, Erik Poll Radboud University Nijmegen, Joost Visser Leiden University | ||