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Hybrid wireless aided volunteer computing paradigm

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Abstract

Big-data acquisition and processing is important in developing value driven machine learning applications. This is challenging in compute resource-constrained scenarios. Compute resource-constrained scenarios arise due to low capacity of installed cloud infrastructure and low availability of high speed internet links. These factors limit the ability to process crowd-sourced data to develop machine learning applications. The volunteer computing paradigm is found to be suitable for addressing these challenges. Volunteer computing paradigm makes use of computing nodes provided by users distributed over a geographical area. It leverages on the availability of volunteers with low cost computing entities. This paper proposes the fractionated computing system (FCS) to address the challenges described above. FCS incorporates intelligent compute node selection and uses high performance end-user computing nodes (laptops) to process the crowd-sourced data. The performance of FCS is investigated against the existing method of using cloud servers. Results show that FCS reduces acquisition costs and power consumption by 35% and up to 56.5% on average, respectively. The watt per bit expended in processing crowd-sourced data is also enhanced by up to 98% on average. In addition, the use of FCS enhances memory resources accessible for data processing. Simulations show that increasing memory in modular computing entities by up to 58.7% enhances memory available across network of modular volunteer computing nodes by 0.5 EB. The use of end-user nodes with modular communication subsystems instead of end-user computing nodes with non-modular communication sub-systems enhances channel capacity by 37.5% on average.

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References

  1. Zhang, N., Yang, P., Ren, J., Chen, D., Yu, L., & Shen, X. (2018). Synergy of big data and 5G wireless networks: Opportunities, approaches and challenges. IEEE Wireless Communications, 25(1), 12–18.

    Article  Google Scholar 

  2. Lin, C., Sun, Q., Liu, Z., Zhang, S., & Han, S. (2017). The big-data-driven intelligent wireless network: Architecture, use cases, solutions, and future trends. IEEE Vehicular Technology Magazine, 12(4), 20–29.

    Article  Google Scholar 

  3. Zhang, G., Li, T., Li, Y., Hui, P., & Jin, D. (2018). Blockchain-based data sharing system for AI-powered network operations. Journal of Communications and Information Networks, 3(3), 1–8.

    Article  Google Scholar 

  4. Huang, Y., Tan, J., & Liang, Y. (2017). Wireless big data: Transforming heterogeneous networks to smart networks. Journal of Communications and Information Networks, 2(1), 19–32.

    Article  Google Scholar 

  5. Li, R., Zhao, Z., Yang, C., Wu, C., & Zhang, H. (2018). Wireless big data in cellular networks: The cornerstone of smart cities. IET Communications, 12(13), 1517–1523.

    Article  Google Scholar 

  6. Abhigna, B. S., Soni, N., & Dixit, S. (2018). Crowdsourcing—A step towards advanced machine learning. Procedia Computer Science, 132, 632–642.

    Article  Google Scholar 

  7. https://alegion.com.

  8. Parameswaran, A., Sarma, A. D., & Venkataraman, V. (2016). Optimizing open—Ended crowdsourcing: The next frontier in crowdsourced data management. The Bulletin of the Technical Committee on Data Engineering, 39(4), 26–37.

    Google Scholar 

  9. Zhou, N., Siagel, Z. D., Zarecor, S., Lee, N., Campbell, D. H., Andorf, C. M., et al. (2018). Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1006337.

    Article  Google Scholar 

  10. Mozafari, B., Sarkar, P., Franklin, M., Jordan, M., & Madden, S. (2014). Scaling up crowd-sourcing to very large dataset: A case for Active Learning. Proceeding of the VLDB Endowment, 8(2), 125–136.

    Article  Google Scholar 

  11. Amolo, G. O. (2018). The growth of high-performance computing in Africa. Computing in Science and Engineering, 20(3), 21–24.

    Article  Google Scholar 

  12. https://www.kenet.or.ke/content/advances-research-end-computing-services-kenet.

  13. Ahmad, A., Paul, A., Din, S., Rathore, M. M., Choi, G. S., & Jeon, G. (2018). Multi-level data processing using parallel algorithms for analysing big data in high performance computing. International Journal of Parallel Programming, 46(3), 508–527.

    Article  Google Scholar 

  14. Zafari, A., & Larsson, E. (2018). Distributed dynamic load balancing for task parallel programming [Online]. Retrieved Aug 1, 2018 from https://arxiv.org/pdf/1801.04582.pdf.

  15. Chen, D., Hu, Y., Cai, C., Zeng, K., & Li, X. (2017). Brain big data processing with massively parallel computing terminology: Challenges and opportunities. Journal of Software Practice & Experience, 47(3), 405–420.

    Article  Google Scholar 

  16. Lavoie, E., & Hendren, L. (2018). Personal volunteer computing [Online]. Retrieved June 28, 2018 from https://arxiv.org/ftp/arxiv/papers/1804/1804.01482.pdf.

  17. www.sona-systems.com.

  18. https://www.mturk.com.

  19. International Telecommunication Union. (2017). Spanning the Internet divide to drive development. In Aid for trade at a glance 2017: Promoting trade, inclusiveness and connectivity for sustainable development. OECD WTO 2017, pp. 143–178.

  20. Vaughan, J. W. (2018). Making better use of the crowd: How crowd-sourcing can advance machine learning research. Journal of Machine Learning Research, 18, 1–46.

    MATH  Google Scholar 

  21. Ross, P. E. (2016). Tesla reveals its crowd-sourced autopilot data. IEEE Spectrum [Online]. Retrieved July 3, 2018 from https://spectrum.ieee.org/cars-that-think/transportation/self-driving/tesla-reveals-its-crowd-sourced-autopilot-data.

  22. Daniel, F., Kurcharbaev, P., Capiello, C., Beratallah, B., & Allahbakhsh, M. (2018). Quality control in crowd-sourcing: A survey of quality attributes, assessment techniques and assurance actions. ACM Computing Surveys, 51, 7.

  23. Thies, W., Ratan, A., & Davis, J. (2018). Paid crowd sourcing as a vehicle for global development [Online]. Retrieved June 20, 2018 from https://www.humancomputation.com/crowdcamp/ch12011/papers/thies.pdf.

  24. Chan, J., Hope, T., Shahaf, D., & Kiltur, A. (2018). Scaling up analogy with crowd sourcing and machine learning [Online]. Retrieved June 20, 2018 from www.cs.huji.ac.il/dshahaf/crowd-machine-learning16.pdf.

  25. www2.acer.com.au/edm/Acer_Revo_Build_Edm_Digital.pdf.

  26. Soo, K. Y., & Escolin, G. T. (2016). Modular computing device. United States Patent Application 20160041582. February 11, 2016.

  27. https://www2.razer.com/christine.

  28. Microsoft. (2018). Microsoft ignite book of news [online]. Retrieved October 21, 2018 from https://news.microsoft.com/uploads/prod/sites/507/2018/09/IGNITEBOOKOFNEWS-5ba8f830261da.pdf.

  29. Lancaster, D. T. (2018). Future surface products could include USB-C webcam, modular PC [Online]. Retrieved Nov 12, 2018 from www.windowscentral.com/future-surface-products-could-include-usb-c-webcam-modular-pc.

  30. Varghese, B., & Buyya, R. (2018). Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79(Part 3), 849–861.

    Article  Google Scholar 

  31. Pisani, F., Rosario, V. M., & Borin, E. (2019). Fog vs. cloud computing: Should I stay or should I go? Future Internet, 11, 34. https://doi.org/10.3390/fi11020034.

    Article  Google Scholar 

  32. Periola, A. (2018). Incorporating diversity in cloud-computing: A novel paradigm and architecture for enhancing the performance of future cloud radio access networks. Wireless Networks. https://doi.org/10.1007/s11276-018-01915-2.

    Article  Google Scholar 

  33. Mahmud, R., Kotagiri, R., & Buyya, R. (2018). Fog computing: A taxonomy, survey and future directions. In B. Di-Martino, K. C. Li, L. Yang, & A. Esposito (Eds.), Internet of everything. Internet of things (Technology, communications and computing) (pp. 103–130). Singapore: Springer.

    Google Scholar 

  34. Lavoie, E., & Hendren, L. (2019) Personal volunteer computing. In Proceedings of the 16th ACM international conference on computer frontiers, Alghero, Italy—April 30–May 02, 2019, pp. 240–246.

  35. Lavoie, E., Hendren, L., Desprez, F., & Correia, M. (2019). Genet: A quickly ScalableFat-tree overlay for personal volunteer computing using WebRTC. In 13th international conference on self-adaptive and self-organizing systems (SASO). arXiv: 1904.11402.

  36. Fang, W., & Beckert, U. (2018). Parallel tree search in volunteer computing: A case study. Journal of Grid Computing, 16, 647–662.

    Article  Google Scholar 

  37. Ignatov, A., & Posypkin, M. (2018). BOINC-based branch-and-bound. In V. Voevodin & S. Sobolev (Eds.), Supercomputing. RuSCDays 2018. Communications in computer and information science, Vol. 965 (pp. 921–932). Berlin: Springer.

    Google Scholar 

  38. Wu, D., Lambrinos, L., Przepiorka, T., Arkhipov, D. I., Liu, Q., Regan, A. C., et al. (2019). Enabling efficient offline mobile access to online social media on urban underground metro systems. IEEE Transactions on Intelligent Transportation Systems (Early Access), 29, 1–15.

    Google Scholar 

  39. Zhao, Y., Li, Y., Wu, D., & Ge, N. (2017). Overlapping coalition formation game for resource allocation in network coding aided D2D communications. IEEE Transactions on Mobile Computing, 16(12), 3459–3472.

    Article  Google Scholar 

  40. Wu, D., Arkhipov, D. I., Kim, M., Talcott, C. L., Regan, A. C., McCann, J. A., et al. (2017). ADDSEN: Adaptive data processing and dissemination for drone swarms in urban sensing. IEEE Transactions on Computers, 66(2), 183–198.

    MathSciNet  MATH  Google Scholar 

  41. Kwesi, A. G. N., & Yirenkyi, P. A. (2008). Volunteer computing: Application for African scientist. In ICT AFRICA 2008. https://ictafrica.nepadcouncil.org/proceedings2008/PR_Volunteer_Computing-Amoako.pdf.

  42. Krebbs, V. (2010). Motivations of cyber-volunteers in an applied distributed computing environment: MalariaControl.net as an example. First Monday, 15(12). https://firstmonday.org/ojs/index.php/fm/article/download/2783/2452.

  43. https://www.climateprediction.net/getting-started/.

  44. Palomaki, R. T., Rose, N. T., Bossche, M., Sherman, T. J., & Walker, S. F. J. (2017). Wind estimation in the lower atmosphere using multirotor aircraft. Journal of Atmospheric and Oceanic Technology, 34, 1183–1191.

    Article  Google Scholar 

  45. Wolf, C. A., Hardis, R. P., Woodrum, S. D., Gaton, R. S., Wichelt, H. S., Metzger, M. C., et al. (2017). Wind data collection techniques on a multi-rotor platform. In Systems and information engineering design symposium, Charlottesville, VA, USA. https://doi.org/10.1109/SIEDS.2017.7937739.

  46. Shimura, T., Inoue, M., Tsujimoto, H., Sasaki, K., & Iguchi, M. (1000m). Estimation of wind vector profile using a hexarotor unmanned aerial vehicle and its application to meteorological observation up to 1000m above surface. Journal of Atmospheric and Oceanic Technology, 35, 1621–1631.

    Article  Google Scholar 

  47. https://www.dell.com/en-us/work/shop/povw/poweredge-r830.

  48. https://www.dell.com/en-us/work/shop/povw/poweredge-r840.

  49. https://www.dell.com/en-us/work/shop/povw/poweredge-r940.

  50. https://www.dell.com/en-us/work/shop/povw/poweredge-r930.

  51. https://www.amazon.com/alienware/anw15-5350slv-15-6-Gaming-Laptop/dp/BOOSIJGGGC.

  52. https://www.dell.com/en-us/shop/dell-laptops.alienware-17-gaming-laptop/spd/alienware-17-laptop.

  53. https://www.dell.com/en-us/shop/dell-laptops.alienware-m15-gaming-laptop/spd/alienware-m15-laptop.

  54. https://www.dell.com/ng/p/alienware-15-r2/pd?ref=D_OC.

  55. https://www.dell.com/ng/p/alienware-17-r3/pd?ref=PD_OC.

  56. Li, F., Liu, X., Lam, K., Na, Z., Hua, J., Wang, J., et al. (2018). Spectrum allocation with asymmetric monopoly model for multibeam-based cognitive satellite networks. IEEE Access, 6, 9713–9722.

    Article  Google Scholar 

  57. Periola, A. A., & Falowo, O. E. (2017). Cognitive communications for commercial networked earth observing fractionated small satellites. Wireless Personal Communications, 97(1), 443–467.

    Article  Google Scholar 

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  1. Department of Electrical, Electronics and Computer Engineering, Bells University of Technology, Otta, Nigeria

    Ayodele A. Periola

  2. Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa

    Olabisi E. Falowo

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  1. Ayodele A. Periola
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Periola, A.A., Falowo, O.E. & Senior Member IEEE. Hybrid wireless aided volunteer computing paradigm. Wireless Netw 26, 5355–5369 (2020). https://doi.org/10.1007/s11276-020-02395-z

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