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Automatic Performance Space Exploration of Web Applications using Genetic Algorithms

Tanwir Ahmad, Dragos Truscan, Automatic Performance Space Exploration of Web Applications using Genetic Algorithms. In: Sascha Ossowski, Giorgio Buttazzo, John Kim (Eds.), The 31st ACM Symposium on Applied Computing, 795 – 800, ACM, 2016.

http://dx.doi.org/10.1145/2851613.2851864

Abstract:

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.

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BibTeX entry:

@INPROCEEDINGS{inpAhTr16a,
  title = {Automatic Performance Space Exploration of Web Applications using Genetic Algorithms},
  booktitle = {The 31st ACM Symposium on Applied Computing},
  author = {Ahmad, Tanwir and Truscan, Dragos},
  editor = {Ossowski, Sascha and Buttazzo, Giorgio and Kim, John},
  publisher = {ACM},
  pages = {795 – 800},
  year = {2016},
}

Belongs to TUCS Research Unit(s): Software Engineering Laboratory (SE Lab)

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