Enhancing Data Processing Efficiency : The Synergy of Edge Computing and Hybrid Cloud Storage

Authors

  • Vamsi Krishna Rao University of Mumbai, India Author

DOI:

https://doi.org/10.32628/CSEIT241051063

Keywords:

Edge Computing, Hybrid Cloud Storage, Real-time Data Processing, Distributed Computing Architecture, IoT Data Management

Abstract

This comprehensive article explores the transformative integration of edge computing and hybrid cloud storage, a technological convergence that is reshaping data processing architectures in the era of exponential data growth. The research delves into the fundamental principles of edge computing and hybrid cloud storage, examining their synergistic relationship in addressing the limitations of traditional centralized cloud computing. By bringing computational resources closer to data sources, this integrated approach significantly reduces latency, enhances processing efficiency by up to 50%, and improves overall system reliability. The article presents detailed case studies in autonomous driving and smart city infrastructure, showcasing real-world applications and benefits. It critically analyzes the challenges inherent in this integration, including security concerns in decentralized architectures, data consistency issues, and cost implications. Furthermore, the article explores future directions, discussing emerging technologies such as AI-powered edge devices, evolving hybrid cloud solutions, and the potential for further optimization. This research provides valuable insights for organizations and researchers navigating the complex landscape of distributed computing, offering a roadmap for leveraging edge computing and hybrid cloud storage to achieve unprecedented levels of performance, scalability, and flexibility in data management and processing.

Downloads

Download data is not yet available.

References

Microsoft Azure, " What is edge computing?," Microsoft, 2024. [Online]. Available: https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-edge-computing/

W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, and A. Ahmed, "Edge computing: A survey," Future Generation Computer Systems, vol. 97, pp. 219-235, 2019. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0167739X18319903 DOI: https://doi.org/10.1016/j.future.2019.02.050

A. Hameed, A. Khoshkbarforoushha, R. Ranjan, P. P. Jayaraman, J. Kolodziej, P. Balaji, S. Zeadally, Q. M. Malluhi, N. Tziritas, A. Vishnu, S. U. Khan, and A. Zomaya, "A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems," Computing, vol. 98, no. 7, pp. 751-774, 2016. [Online]. Available: https://link.springer.com/article/10.1007/s00607-014-0407-8 DOI: https://doi.org/10.1007/s00607-014-0407-8

W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge Computing: Vision and Challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, 2016. [Online]. Available: https://ieeexplore.ieee.org/document/7488250 DOI: https://doi.org/10.1109/JIOT.2016.2579198

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the internet of things," Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13-16, 2012. [Online]. Available: https://dl.acm.org/doi/10.1145/2342509.2342513 DOI: https://doi.org/10.1145/2342509.2342513

W. Yu, F. Liang, X. He, W. G. Hatcher, C. Lu, J. Lin, and X. Yang, "A Survey on the Edge Computing for the Internet of Things," IEEE Access, vol. 6, pp. 6900-6919, 2018. [Online]. Available: https://ieeexplore.ieee.org/document/8123913 DOI: https://doi.org/10.1109/ACCESS.2017.2778504

Downloads

Published

01-11-2024

Issue

Section

Research Articles

How to Cite

[1]
Vamsi Krishna Rao, “Enhancing Data Processing Efficiency : The Synergy of Edge Computing and Hybrid Cloud Storage”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 5, pp. 762–771, Nov. 2024, doi: 10.32628/CSEIT241051063.

Similar Articles

1-10 of 354

You may also start an advanced similarity search for this article.