• Satyanarayan Kanungo

Cloud Computing There is a path to rapid growth, and it revolutionizes the way we do it. Businesses access and use computing resources. However, as cloud infrastructures become more complex and dynamic, optimizing cloud performance has become a key challenge. This study proposes a new framework that leverages the power of machine learning to improve cloud performance through intelligent resource allocation and predictive analytics. The framework's core is a dynamic resource provisioning strategy based on advanced machine learning models. These models analyze real-time performance data and system metrics to make adaptive resource allocation decisions to ensure optimal utilization and minimize performance bottlenecks. By continuously learning from the cloud environment, the framework adapts to changing workloads and user requirements, delivering consistently high performance. This study also introduces a predictive analytics component that uses machine learning techniques to predict cloud performance metrics. This allows cloud service providers to proactively identify potential issues and take preventive measures to meet service level agreements and maintain customer satisfaction. Extensive experiments on a realistic cloud test bed confirm the effectiveness of the proposed framework. The results show significant improvements in key performance metrics such as response time, resource utilization, and energy efficiency compared to traditional cloud optimization approaches. Furthermore, this framework has been successfully deployed in a real cloud environment, demonstrating its practical applicability and adaptability. The results of this study contribute to the advancement of cloud computing by providing a comprehensive solution for intelligent performance optimization. The framework's ability to harness the power of machine learning paves the way for more autonomous, resilient, and adaptable cloud infrastructures that meet the ever-growing demands of modern computing environments. This study's insights provide directions for future research and assist cloud service providers in seeking to improve cloud performance and user satisfaction.

Cloud performance optimization, Machine learning, Intelligent resource allocation, Predictive analytics, Cloud computing, Performance enhancement, Resource management.

"Enhancing Cloud Performance with Machine Learning: Intelligent Resource Allocation and Predictive Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.32-38, June-2020, Available :http://www.jetir.org/papers/JETIR2006612.pdf

"Enhancing Cloud Performance with Machine Learning: Intelligent Resource Allocation and Predictive Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp32-38, June-2020, Available at : http://www.jetir.org/papers/JETIR2006612.pdf