An Extensive Analysis of Resource Distribution in Cloud Computing Settings

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Dr. Gopal Sharma

An Extensive Analysis of Resource Distribution in Cloud Computing Settings

Authors: Dr. Gopal Sharma

Unique Paper ID: 170656
Volume: 11
Issue: 7
PageNo: 495-500

Keywords: loud computing resource distribution load balancing energy efficiency machine learning multi-cloud environments adaptive systems sustainability predictive analytics hybrid cloud.

Abstract:
is study providing a comprehensive analysis of resource allocation mechanisms and their impact on system efficiency, focusing on key challenges such as load balancing, latency reduction, energy efficiency, and resource overprovisioning. It explores state-of-the-art algorithms and strategies, including heuristic, metaheuristic, and AI-based approaches, while addressing trade-offs between computational efficiency and system complexity. Furthermore, this paper evaluates resource allocation performance in multi-cloud and hybrid-cloud environments, highlighting real-world case studies and experimental results. The findings aim to guide the development of robust, adaptive resource distribution frameworks that enhance service delivery while minimizing operational costs. The study underscores the importance of integrating advanced predictive analytics and machine learning to foster intelligent, self-optimizing cloud ecosystems.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 7
PageNo: 495-500

An Extensive Analysis of Resource Distribution in Cloud Computing Settings

Available:https://ijirt.org/article?manuscript=170656

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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