About one Approach to Organizing a Home Distributed Computing System

Sergey Balabaev, Sergey Lupin, Dmitry Fedyashin

Abstract


The paper considers an approach to organizing a home distributed computing system consisting of a personal computer and an integrated smartphone, a Raspberry Pi microcomputer, and a Smart TV set-top box. An overview of existing software solutions is given - clusters of smartphones and Raspberry Pi microcomputers. It is noted that the use of low-power devices as nodes of a distributed system is possible, but is accompanied by difficulties in developing and configuring software. Based on the analysis, the functionality was determined and software for integrating mobile devices with a PC was developed. It allows you to upload a calculation task to the nodes, run it, accumulate and display the obtained calculation results on the screen. Interaction between devices occurs over a local network. The software for mobile devices is developed in Java. The paper proposes a method for running scripts written in Python on smartphones using the Termux application. The used node load balancing algorithm allows you to combine devices with significantly different performance into a single environment. The article presents the results of solving the problem of federated training of a neural network in a distributed environment organized using the developed software. They confirm the functionality of the developed software and the possibility of using a distributed system organized with its help with heterogeneous nodes to solve optimization problems.

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References


Pramanik P. K. D., Pal S., Choudhury P. Mobile crowd computing: potential, architecture, requirements, challenges, and applications //The Journal of Supercomputing. - 2024. - Vol. 80. - No. 2. - P. 2223-2318. URL: https://doi.org/10.1007/s11227-023-05545-0

S. A. Balabaev, "Evaluation of computing capabilities of mobile platforms," 28th All-Russian Interuniversity Scientific and Technical Conference of Students and Postgraduates "Microelectronics and Computer Science - 2021", 2021.

Balabaev S. A., Lupin S. A., Taik A. M. Monitoring system for load balancing nodes of a distributed computing system based on smartphones //International Journal of Open Information Technologies. – 2024. – Vol. 12. – No. 10. – P. 78-85.

Khaing M., Lupin S. A., Thu A. Evaluating the effectiveness of load balancing methods in distributed computing systems // International Journal of Open Information Technologies. – 2021. – Vol. 9. – No. 11. – P. 30-36.

Takawale H. C., Thakur A. Talos app: on-device machine learning using tensorflow to detect android malware // 2018 fifth international conference on Internet of Things: systems, management and security. – IEEE, 2018. – P. 250-255. URL: http://dx.doi.org/10.1109/IoTSMS.2018.8554572

Salem H. Distributed computing system on a smartphones-based network //Software Technology: Methods and Tools: 51st International Conference, TOOLS 2019, Innopolis, Russia, October 15–17, 2019, Proceedings 51. – Springer International Publishing 201 9 URL: http://dx.doi.org/10.1007/978-3-030-29852-4_26

Kaushik P., Yadav P. K. A novel approach for detecting malware in android applications using deep learning //2018 Eleventh International Conference on Contemporary Computing (IC3). – IEEE, 2018. – pp. 1-4. https://doi.org/10.1109/IC3.2018.8530668

Fang W. et al. Comprehensive android malware detection based on federated learning architecture //IEEE Transactions on Information Forensics and Security. – 2023. – T. 18. – P. 3977-3990. https://doi.org/10.1109/TIFS.2023.3287395

Tang J. et al. PE-FedAvg: A Privacy-Enhanced Federated Learning for Distributed Android Malware Detection //2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). – IEEE, 2023. – pp. 474-481 https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00094

Kurochkin I. et al. Using Mobile Devices in a Voluntary Distributed Computing Project to Solve Combinatorial Problems //Supercomputing: 7th Russian Supercomputing Days, RuSCDays 2021, Moscow, Russia, September 27–28, 2021, Revised Selected Papers 7. – Springer International Publishing, 2021. – pp. 525-537. http://dx.doi.org/10.1007/978-3-030-92864-3_40

Dolgov A. A. Deployment of a Grid System from Mobile Devices on the BOINC Platform // Cloud and Distributed Computing Systems in Electronic Management of ORVSEU-2022 within the Framework of the National Supercomputer Forum (NSCF-2022), 2022 pp. 24-29

Kurochkin I. I., Prun A. I. Grid System from Personal Devices on the BOINC Platform for Solving Deep Learning Problems // Optical-electronic Devices and Devices in Pattern Recognition and Image Processing Systems: Collection of Materials of the XVII International Scientific and Technical Conference, Kursk, September 12-15, 2023. Kursk: South-West State University, 2023. pp. 252-254.

Kurochkin I. I. Decentralized deep learning on a grid system of personal computers // Proceedings of the XXIII International Conference on Computational Mechanics and Modern Applied Software Systems (VMSPPS'2023), p. Divnomorskoye, Krasnodar Krai, September 4-10, 2023. Moscow: Moscow Aviation Institute (National Research University), 2023. Pp. 111-113.

Official website of the BOINC project [Electronic resource] / URL: https://boinc.berkeley.edu/russia.php (Accessed: 11/30/24)

Gurusamy V., Nandhini K. International journal of engineering sciences & research technology IBIS: The new era for distributed computing. http://dx.doi.org/2018 10.5281/zenodo.1135392

Palmer N. et al. Ibis for mobility: solving challenges of mobile computing using grid techniques //Proceedings of the 10th workshop on Mobile Computing Systems and Applications. – 2009. – P. 1-6. http://dx.doi.org/10.1145/1514411.1514426

Kumar T. U., Senthilkumar R. CWC* - Secured distributed computing using Android devices //2016 International Conference on Recent Trends in Information Technology (ICRTIT). – IEEE, 2016. – pp. 1-7 https://doi.org/10.1109/ICRTIT.2016.7569590

Arslan M. Y. et al. Computing while charging: Building a distributed computing infrastructure using smartphones //Proceedings of the 8th International conference on Emerging networking experiments and technologies. – 2012. – P. 193-204. https://doi.org/10.1145/2413176.2413199

Balabaev S. A., Lupin S. A., Sha Kirov R. N. Computing cluster based on Android smartphones and Raspberry Pi microcomputers // International Journal of Open Information Technologies. - 2022. - Vol. 10. - No. 7. - P. 86-93.

Komninos A. et al. Performance of raspberry pi microclusters for edge machine learning in tourism // Network (Mbps). - 2019. - Vol. 100. - No. 100. - P. 100.

Xu Z. Teaching heterogeneous and parallel computing with google colab and raspberry pi clusters // Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. – 2023. – P. 308-313 https://doi.org/10.1145/3624062.3624095

Govindaraj, Parallel Programming in Raspberry Pi Cluster, Ithaca, 2016

Balabaev S.A., Lupin S.A. Evaluation of the functionality of a cluster of a personal computer and mobile devices / IV scientific and practical conference with international participation "Actual problems of informatization in the digital economy and scientific research - 2023", Zelenograd. 2023.

Balabaev S.A., Gureev A.V. Comparison of methods for developing a heterogeneous cluster of mobile devices on the Android platform. // 29th All-Russian Interuniversity Scientific and Technical Conference of Students and Postgraduates "Microelectronics and Computer Science - 2022" Zelenograd. 2022.

Balabaev S.A., Balabaev A.A., Application of CoMD systems for training neural networks // 31st All-Russian Interuniversity Scientific and Technical Conference of Students and Postgraduates "Microelectronics and Computer Science - 2024" Zelenograd. 2024.


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