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Cloud Development and Deployment

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Optimising the Software Development Process with Artificial Intelligence

Part of the book series: Natural Computing Series ((NCS))

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Abstract

This chapter describes the challenges related to the software deployment and configuration activities, which could be addressed through AI techniques. Additionally, we illustrate the application of such techniques with two specific problems: the QoS-aware binding of web services, and the selection of the optimal configuration in highly-configurable systems. The AI techniques applied to solve such problems are GRASP with Path Relinking and Multi-Objective Evolutionary Algorithms, respectively. These techniques will be applied to deployment scenarios based on empirically obtained data from real-world web services and highly-configurable software systems. These examples are implemented in Java and C, respectively, providing the source code, build scripts and data sets for the sake of reproducibility.

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Notes

  1. 1.

    For instance the availability of Dropbox was taken form https://digitalfirst.com/dropbox-availability-finally-revealed/.

  2. 2.

    Note that it makes no sense to explore more neighbours since the maximum size of the paths is 3 (the number of different services to bind).

  3. 3.

    https://pantheon.io/blog/drupal-service.

  4. 4.

    https://git.drupalcode.org/project/drupal.

  5. 5.

    https://www.drupal.org/project/issues.

  6. 6.

    Editors note: The source code linked in this chapter could change after publishing this book. A snapshot of the source code accompanying this chapter can be found at https://doi.org/10.5281/zenodo.6965479.

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Acknowledgements

Authors of this chapter are especially thankful to Miqing Li, which provided the C implementation of the NSGA-II algorithm applied to solve the Optimal Configuration Problem. Additionally, the authors are thankful to Sergio Segura for their support during the elaboration of this chapter. This work has been partially supported by FEDER/Ministerio de Ciencia e Innovación—Agencia Estatal de Investigación under projects HORATIO (RTI2018101204-B-C21) and OPHELIA (RTI2018101204-B-C22); and by FEDER/Junta de Andalucía under the project APOLO (US-1264651). PID2021-126227NB-C22 funded by MCIN/AEI/10.13039/501100011033/FEDER, UE and by grant TED2021-131023B-C21 funded by MCIN/AEI/10.13039/501100011033 and by NextGeneration EU.

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Authors and Affiliations

  1. Smart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), Universidad de Sevilla, Seville, Spain

    José Antonio Parejo & Ana Belén Sánchez

Authors
  1. José Antonio Parejo
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  2. Ana Belén Sánchez
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Correspondence to José Antonio Parejo .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Córdoba, Spain

    José Raúl Romero

  2. Department of Computer Science and Engineering, University of Cádiz, Puerto Real, Cádiz, Spain

    Inmaculada Medina-Bulo

  3. ITIS Software, Department of Languages and Computing Sciences, University of Malaga, Málaga, Málaga, Spain

    Francisco Chicano

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Antonio Parejo, J., Sánchez, A.B. (2023). Cloud Development and Deployment. In: Romero, J.R., Medina-Bulo, I., Chicano, F. (eds) Optimising the Software Development Process with Artificial Intelligence. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-19-9948-2_6

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  • DOI: https://doi.org/10.1007/978-981-19-9948-2_6

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  • Online ISBN: 978-981-19-9948-2

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