Cloud Computing Strategies for Large-Scale Data Management in High-Performance Applications |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Authors : Rakesh Kumar Mali | ||
| DOI : 10.14445/22312803/IJCTT-V73I3P117 | ||
How to Cite?
Rakesh Kumar Mali, "Cloud Computing Strategies for Large-Scale Data Management in High-Performance Applications," International Journal of Computer Trends and Technology, vol. 73, no. 3, pp. 133-141, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I3P117
Abstract
High-performance application data continues to grow exponentially, requiring strong, scalable and efficient data management. Its problem-solving offerings are primarily distributed storage, elastic computing, and real-time analytics capabilities. Running large data sets inside cloud structures makes a case for remedies to four central roadblocks: protecting information, postponing information moves, controlling expenses and ensuring the framework is ready to go. The paper examines significant cloud strategies for fast-performance applications, focused on big data management through multi-cloud deployments, hybrid cloud modalities, audio serverless computing and AI-based data orchestration systems. This analysis decorates the inquests of ageing edge computing with distributed ledger technology to raise data availability while protecting its trustworthiness. From a statement investigation, the report represents performance indicators with practical implementations and optimizations ensuring proper utilization of cloud systems to address complex dataset requirements. Focused on the need for speed processing and scalable solutions, the research presents opportunities to evaluate optimal frameworks for cloud data management via sifting through current challenges of the issues arising from the state of the art in examination for the industrial requirements.
Keywords
Cloud Computing, High-Performance Applications, Large-Scale Data Management, Multi-Cloud, Edge Computing, Serverless Computing, Data Security.
Reference
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