AI-Driven Cloud Optimization: A Comprehensive Literature Review |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-5 |
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| Year of Publication : 2024 | ||
| Authors : Harshavardhan Nerella, Prasanna Sai Puvvada, Sivanagaraju Gadiparthi | ||
| DOI : 10.14445/22312803/IJCTT-V72I5P121 | ||
How to Cite?
Harshavardhan Nerella, Prasanna Sai Puvvada, Sivanagaraju Gadiparthi, "AI-Driven Cloud Optimization: A Comprehensive Literature Review," International Journal of Computer Trends and Technology, vol. 72, no. 5, pp. 177-181, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I5P121
Abstract
The use of AI-driven cloud optimization aims to revolutionize the landscape of cloud services into a highly efficient, scalable, and high-performing technology. This survey paper will cover the multi-faceted dimensions of AI-driven cloud optimization, from foundational technologies and practical applications to current challenges, future trends, and opportunities. First, it conducts a thorough review of the underlying key concepts and tools that empower the proper integration of AI in cloud computing. It is succeeded by the review of successful case studies, which are apparent across a number of industries and the implications these case studies expose with respect to the huge benefits and potential transformation that AI-driven approaches can bring. Some of the challenges in the adoption of AI technologies to cloud infrastructures include ensuring data privacy, high computational costs, and algorithmic bias. For instance, emerging technologies and new research areas are likely to promote the use of scalable AI frameworks, edge computing, and the convergence of computing with communications, which all promise increased capabilities and reach by cloud services. A comprehensive study shows a new perspective regarding the development of the field of artificial intelligence applied to cloud computing and clearly demonstrates the leading role that permanent innovation plays in propelling a new generation of cloud optimization solutions.
Keywords
Cloud computing artificial intelligence, Resource allocation, Machine Learning, Performance optimization, edge computing, Scalable infrastructure.
Reference
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