Arseam

International Journal of Advances in Engineering & Scientific Research

INTEGRATING DISTRIBUTED SYSTEMS INTO PLATFORM ENGINEERING: CHALLENGES AND SOLUTIONS IN HIGH-TRAFFIC APPLICATIONS

Purushottam Raj

Purushottam Raj

Sr Software Engineering Leader India.

DOI : https://doi.org/10.53882/IJAESR.2024.1102001 Page No : 01-15

Published Online : 2024-12-10

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Over the last several decades, computing paradigms have changed dramatically, going from massive room-sized resources (processors and memory) to minuscule computer nodes. Most modern application sectors have been lured to computers' potential. Cloud, fog/edge, IoT, and mobile device computing are currently being combined into a seamless continuum via distributed computing continuum systems (DCCSs). Its seamless design efficiently manages a range of processing needs while ensuring a consistent user experience. Additionally, it offers a comprehensive answer to the demands of contemporary computing. In light of this, this study offers a more thorough comprehension of DCCSs' possibilities in the current computing environment. We start by going over how computing paradigms have changed up to DCCS. The advantages and disadvantages of every computer paradigm are examined, along with generic designs, components, and different computing devices. Following that, we continue our conversation on the different computing devices that make up DCCS in order to accomplish computational objectives in both present-day and future applications. Finally, we discuss the unresolved challenges and potential future advancements required for DCCS to achieve its full potential across a wide range of applications.

Keywords: Internet of Things, edge computing, edge intelligence, artificial intelligence, machine learning, distributed computing continuum systems

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Raj P. (2024), “Integrating Distributed Systems Into Platform Engineering: Challenges And Solutions In High-Traffic Applications”, International Journal of Advances in Engineering & Scientific Research,Volume 11, Issue 2, 2024, pp 01-15. DOI : https://doi.org/10.53882/IJAESR.2024.1102001

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REFERENCES

Alsamhi, S.H.; Shvetsov, A.V et. al (2022). Computing in the sky: A survey on intelligent ubiquitous computing for uav-assisted 6g networks and industry 4.0/5.0. Drones 2022, 6, 177. DOI: https://doi.org/10.3390/drones6070177
Baker, M.; Buyya, R.(1999). Cluster computing: The commodity supercomputer. Soft. Pract. Exp. 1999, 29, 551–576. DOI: https://doi.org/10.1002/(SICI)1097-024X(199905)29:6%3C551::AID-SPE248%3E3.0.CO;2-C
Bharathy, S. S. P. D., Preethi, P., et. al (2017). Hand Gesture Recognition for Physical Impairment Peoples. SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE), 6-10. Retrieved From: file:///C:/Users/arseam/Downloads/Article64.pdf
Casanova, H. (2022). Distributed computing research issues in grid computing. ACM SIGAct News 2002, 33, 50–70. DOI: https://doi.org/10.1145/582475.582486
De Donno, M.; Tange, K.; Dragoni, N. (2019). Foundations and evolution of modern computing paradigms: Cloud, iot, edge, and fog. IEEE Access 2019, 7, 150936–150948. DOI: https://doi.org/10.1109/ACCESS.2019.2947652
Greenstein, S.M . (1997). Lock-in and the costs of switching mainframe computer vendors: What do buyers see? Ind. Corp. Chang. 1997, 6, 247–273. Retrieved From: https://ideas.repec.org/a/oup/indcch/v6y1997i2p247-73.html
Iansiti, M.; Clark, K.B. (1994). Integration and dynamic capability: Evidence from product development in automobiles and mainframe computers. Ind. Corp. Chang. 1994, 3, 557–605. DOI: https://doi.org/10.1093/icc/3.3.557
Korada, L. (2024). Data Poisoning-What Is It and How It Is Being Addressed by the Leading Gen AI Providers. European Journal of Advances in Engineering and Technology, 11(5), 105-109. DOI: http://dx.doi.org/10.5281/zenodo.13318796
Laxminarayana Korada, D. M. K., et. al (2024). Artificial Intelligence on The Administration Of Financial Markets. DOI: https://doi.org/10.62441/nano-ntp.vi.2493
Rekha, P., Saranya, T., Preethi, P., et. al (2017). Smart Agro Using Arduino and GSM. International Journal of Emerging Technologies in Engineering Research (IJETER) Volume, 5. Retrieved From: https://www.ijeter.everscience.org/Manuscripts/Volume-5/Issue-3/Vol-5-issue-3-M-09.pdf
Schwiegelshohn, U.; Badia, R et al. (2010). Perspectives on grid computing. Future Gener. Comput. Syst. 2010, 26, 1104–1115. DOI: https://doi.org/10.1016/j.future.2010.05.010
Singh, S. K., Choudhary, S. K., et. al (2022). Comparative Analysis of Machine Learning Models and Data Analytics Techniques for Fraud Detection in Banking System. International Journal of Core Engineering & Management, 7(1), 64. ISSN 2348-9510. Retrieved From: https://www.researchgate.net/publication/384678305_COMPARATIVE_ANALYSIS_OF_MACHINE_LEARNING_MODELS_AND_DATA_ANALYTICS_TECHNIQUES_FOR_FRAUD_DETECTION_IN_BANKING_SYSTEM
Sujithra, M., Velvadivu, P., Rathika, J., (2022). A Study on Psychological Stress Of Working Women In Educational Institution Using Machine Learning. In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/ICCCNT54827.2022.9984460
Suresh, K., Reddy, P. P., et. al (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520. DOI: http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1515-1520
Yeo, C.S.; Buyya, R. (2006). A taxonomy of market-based resource management systems for utility-driven cluster computing. Softw. Pract. Exp. 2006, 36, 1381–1419. DOI: https://doi.org/10.1002/spe.725
Yu, J.; Buyya, R. (2005). A taxonomy of workflow management systems for grid computing. J. Grid Comput. 2005, 3, 171–200. Retrieved From: https://link.springer.com/article/10.1007/s10723-005-9010-8
Yuan, J.; Xiao, H.; Shen, Z.; et. al (2023). ELECT: Energy-efficient intelligent edge–cloud collaboration for remote IoT services. Future Gener. Comput. Syst. 2023, 147, 179–194. DOI: https://doi.org/10.1016/j.future.2023.04.030

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