Automatic Performance Space Exploration of Web Applications using Genetic Algorithms

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Tanwir Ahmad, Dragos Truscan
Editors: Sascha Ossowski, Giorgio Buttazzo, John Kim
Publication year: 2016
Publisher: ACM
Book title: SAC '16 Proceedings of the 31st Annual ACM Symposium on Applied Computing
Title of series: SAC
Start page: 795
End page: 800
ISBN: 978-1-4503-3739-7


We describe a tool-supported performance exploration approach in which we use genetic algorithms to find a potential user behavioural pattern that maximizes the resource utilization of the system under test. This work is built upon our previous work in which we generate load from workload models that describe the expected behaviour of the users. In this paper, we evolve a given probabilistic workload model (specified as a Markov Chain Model) by optimizing the probability distribution of the edges in the model and generating different solutions. During the evolution, the solutions are ranked according to their fitness values. The solutions with the highest fitness are chosen as parent solutions for generating offsprings. At the end of an experiment, we select the best solution among all the generations. We validate our approach by generating load from both the original and the best solution model, and by comparing the resource utilization they create on the system under test.


genetic algorithms, Markov Chain model, Performance exploration, performance testing


Last updated on 2020-08-08 at 06:31