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Running Genetic Algorithms in the Edge: A First Analysis

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Advances in Artificial Intelligence (CAEPIA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11160))

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Abstract

Nowadays, the volume of data produced by different kinds of devices is continuously growing, making even more difficult to solve the many optimization problems that impact directly on our living quality. For instance, Cisco projected that by 2019 the volume of data will reach 507.5 zettabytes per year, and the cloud traffic will quadruple. This is not sustainable in the long term, so it is a need to move part of the intelligence from the cloud to a highly decentralized computing model. Considering this, we propose a ubiquitous intelligent system which is composed by different kinds of endpoint devices such as smartphones, tablets, routers, wearables, and any other CPU powered device. We want to use this to solve tasks useful for smart cities. In this paper, we analyze if these devices are suitable for this purpose and how we have to adapt the optimization algorithms to be efficient using heterogeneous hardware. To do this, we perform a set of experiments in which we measure the speed, memory usage, and battery consumption of these devices for a set of binary and combinatorial problems. Our conclusions reveal the strong and weak features of each device to run future algorihms in the border of the cyber-physical system.

This research has been partially funded by the Spanish MINECO and FEDER projects TIN2014-57341-R (http://moveon.lcc.uma.es), TIN2016-81766-REDT (http://cirti.es), TIN2017-88213-R (http://6city.lcc.uma.es), the Ministry of Education of Spain (FPU16/02595), and Universidad de Málaga.

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Notes

  1. 1.

    https://www.htc.com/es/go/power-to-give/.

  2. 2.

    https://news.samsung.com/global/power-sleep-app-lets-you-be-a-part-of-an-advanced-scientific-research.

  3. 3.

    http://neo.lcc.uma.es/software/jcell/.

  4. 4.

    http://www.antutu.com/en/.

  5. 5.

    https://www.geekbench.com/.

  6. 6.

    http://www.roylongbottom.org.uk/.

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Author information

Authors and Affiliations

  1. Departamento de Lenguajes y Ciencias de la Computación, Andalucía Tech, Universidad de Málaga, Málaga, Spain

    José Á. Morell & Enrique Alba

Authors
  1. José Á. Morell
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  2. Enrique Alba
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Corresponding author

Correspondence to José Á. Morell .

Editor information

Editors and Affiliations

  1. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Francisco Herrera

  2. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Damas

  3. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Rosana Montes

  4. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Alonso

  5. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Óscar Cordón

  6. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Antonio González

  7. School of Engineering, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso

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Morell, J.Á., Alba, E. (2018). Running Genetic Algorithms in the Edge: A First Analysis. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-00374-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00373-9

  • Online ISBN: 978-3-030-00374-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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