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

Vamsi Krishna Rao

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 5, 762-771

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.

Keywords: Edge Computing; Hybrid Cloud Storage; Real-time Data Processing; Distributed Computing Architecture; IoT Data Management (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241051063
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i5:id:366

DOI: 10.32628/CSEIT241051063

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