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Distributed Computing System on a Smartphones-Based Network

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Abstract

The number of Smartphone users in the world is expected to pass the five billion in 2019. The major credit for this exponential growth is the competition between Smartphones manufacturing companies and increasing Internet availability in the world. Processing power considered to be one of the most important features in Smartphones and it is evolving year by year. Until now, building a distributed computing system done exclusively using PCs and other server infrastructure. In this paper we will propose a new architecture for a distributed computing system consists of a network of Smartphones and use their computation power to execute machine learning models on each Smartphone. As proof of concept, our solution will provide a stable layer to execute large data-sets using common machine learning algorithms such as Linear Regression.

Keywords

  • Distributed system
  • Computation power
  • JS-Regression

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References

  1. Zhang, C., Patras, P., Haddadi, H.: Deep learning in mobile and wireless networking: a survey. IEEE Commun. Surv. Tutorials. arXiv:1803.04311, March 2018

  2. Cisco: Cisco visual networking index: forecast and methodology, June (2017). https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html

  3. Sun, Y., Peng, M., Zhou, Y., Huang, Y., Mao, S.: Application of machine learning in wireless networks: key techniques and open issues. arXiv:1809.08707, September 2018

  4. Han, S., Chih-Lon, I., Li, G., Wang, S., Sun, Q.: Big data enabled mobile network design for 5G and beyond. IEEE Commun. Mag. 55(9), 150–157 (2017)

    CrossRef  Google Scholar 

  5. Dobriban, E., Shengy, Y.: Distributed linear regression by averaging. arXiv:1810.00412, October 2018

  6. Bader, D.A., Pennington, R.: Applications. Int. J. High Perform. Comput. Appl. 15(2), 181–185 (2001)

    CrossRef  Google Scholar 

  7. Bakery, M., Buyya, R.: Cluster computing at a glance, Chapter One, p. 4, September (2000)

    Google Scholar 

  8. Mengy, X.: Machine learning in apache spark. J. Mach. Learn. Res. 17(34), 17 (2016)

    MathSciNet  Google Scholar 

  9. Bala, K., Sharma, S., Kaur, G.: A study on smartphone based operating system. Int. J. Comput. Appl. (0975–8887) 121(1) (2015)

    CrossRef  Google Scholar 

  10. Top machine learning mobile apps \(\bullet \) appy pie. https://www.appypie.com/top-machine-learning-mobile-apps. Accessed 16 Apr 2019

  11. Takawale, H., Thakur, A.: Talos App: on-device machine learning using tensor flow to detect android malware. In: MCSMS (2018)

    Google Scholar 

  12. Yang, K., Jiang, T., Shi, Y., Ding, Z.: Federated learning via over-the-air computation. arXiv 1812(11750) (2018)

    Google Scholar 

  13. Galaxy note features; Samsung phones. https://www.samsung.com/ph/ Smartphones/gal-axy-note8/. Accessed 16 Apr 2019

  14. Burd, G.: NoSQL (2011)

    Google Scholar 

  15. What is ionic framework? http://ionicframework.com/. Accessed 16 Apr 2019

  16. Ionic framework angular JS on the rise. https://blog.codecentric.de/en/2014/11/ionic-angularjs-framework-on-the-rise/. Accessed 16 Apr 2019

  17. Jin, J., Li, M., Jin, L.: Data normalization to accelerate training for linear neural net to predict tropical cyclone tracks. Math. Probl. Eng. 2015, 8 (2014). Hindawi Publishing Corporation

    Google Scholar 

  18. Percentage of US population that own an iPhone smartphone. https://www.statista.com/statistics/236550/percentage-of-us-population-that-own-a-iphone-smartphone. Accessed 16 Apr 2019

  19. Sureddy, S., Rashmi, K., Gayathri, R., Nadhan, A.S.: Flexible deep learning in edge computing for IoT. Int. J. Pure Appl. Math. 119(10), 531–543 (2018)

    Google Scholar 

  20. Strugar, D., Hussain, R., Mazzara, M., Rivera, V., Afanasyev, I., Lee, J.Y.: An architecture for distributed ledger-based M2M auditing for electric autonomous vehicles. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds.) WAINA 2019. AISC, vol. 927, pp. 116–128. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15035-8_11

    CrossRef  Google Scholar 

  21. Burns, B.: Designing Distributed Systems, pp. 80–81. O’Reilly Media Inc., Sebastopol (2018). ISBN: 9781491983638

    Google Scholar 

  22. Zhu, G., Liu, D., Du, Y., You, C., Zhang, J., Huang, K.: Towards an intelligent edge: wireless communication meets machine learning. arXiv preprint 1809.00343 (2018)

  23. Unified analytics engine for big data. https://spark.apache.org/. Accessed 6 June 2019

  24. Guide to Tenserflow. https://www.tensorflow.org/guide/. Accessed 6 June 2019

  25. Richards, M.: Software Architecture Patterns, pp. 54–55. O’Reilly Media Inc., Sebastopol (2015). ISBN: 9781491971437

    Google Scholar 

  26. Lunt, M.: Introduction to statistical modelling: linear regression. Rheumatology 54(7), 1137–1140 (2015)

    CrossRef  Google Scholar 

  27. Js-Regression. https://github.com/chen0040/js-regression. Accessed 6 June 2019

  28. Firebase. https://firebase.google.com. Accessed 6 June 2019

  29. JavaScript Object Notation (JSON). https://json.org. Accessed 6 June 2019

  30. Marinelli, E.: Hyrax: cloud computing on mobile devices using mapreduce. Master’s thesis, CMU, USA (2009)

    Google Scholar 

  31. Remédios, Diogo, Teófilo, António, Paulino, Hervé, Lourenço, João: Mobile Device-to-Device Distributed Computing Using Data Sets. 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (2015)

    Google Scholar 

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Correspondence to Hamza Salem .

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Salem, H. (2019). Distributed Computing System on a Smartphones-Based Network. In: Mazzara, M., Bruel, JM., Meyer, B., Petrenko, A. (eds) Software Technology: Methods and Tools. TOOLS 2019. Lecture Notes in Computer Science(), vol 11771. Springer, Cham. https://doi.org/10.1007/978-3-030-29852-4_26

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  • DOI: https://doi.org/10.1007/978-3-030-29852-4_26

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