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Integrating AI with cloud computing: A framework for scalable and intelligent data processing in distributed environments
Independent Researcher, USA.
Research Article
International Journal of Science and Research Archive, 2022, 06(02), 280–291.
Article DOI: 10.30574/ijsra.2022.6.2.0119
Publication history:
Received on 15 April 2022; revised on 24 June 2022; accepted on 28 June 2022
Abstract:
This paper examines AI concerning cloud computing to establish how it can be used to build a framework for intelligent data processing on a large scale in distributed systems. As the size and density of data continue to extend across the globe, industries require novel and efficient ways of processing the data intensively and in a real fashion. Cloud computing provides more open architecture and scalability, and AI makes it possible for organizations to process big data, make reasonable conclusions, and take the proper actions faster. Instead, the problem is practical planning to integrate these technologies into a seamless, highly performing, affordable, and scalable solution.
The proposed framework features a three-layered architecture: a data layer for data delivery, a Processing Layer for computing, and an Outcome Layer for application execution and insight rendering to the user. This architecture is designed to utilize various cloud services, including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud, to allocate resources dynamically and efficiently manage workloads.
The research establishes that the framework enhances data processing in terms of throughput and decreased latency, resource utilization, and reduced operational expenses. The results show that not only does the application of AI improve scalability alongside cloud computing, but it also assists businesses in making better decisions using data. This thesis brings a practical solution in cloud computing and artificial intelligence to narrow the present-day data processing problem in a distributed environment.
Keywords:
AI; Cloud computing; Scalability; Distributed systems; Intelligent data processing; Machine learning
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
