A review on machine learning models for quality of service in cloud computing | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. Nisha: Department of computer science GJUS&T, Hisar

2. Dr. Deepak Nandal: Department of computer science GJUS&T, Hisar

3. Dr. Sunil Kumar Nandal: Department of computer science GJUS&T, Hisar

Abstract:

One of the most popular ways that services are now provided via the internet is through cloud computing. Factors including resource contention, fluctuating workloads, and varied user needs make guaranteeing QoS in cloud settings a key task. In this research, we provide a new approach that uses machine learning to improve cloud computing's quality of service. The model uses state-of-theart machine learning algorithms to assess past data, forecast future workload patterns, and distribute resources dynamically to successfully fulfill quality-of-service criteria. By continuously learning from past performance metrics and user feedback, the proposed model adapts to changing conditions, optimizing resource utilization and improving service reliability. We conduct comprehensive experiments using real-world cloud datasets to evaluate the performance of our model. Results demonstrate significant improvements in key QoS metrics such as response time, throughput, and availability compared to traditional approaches. Our findings highlight the potential of machine learning techniques in addressing QoS challenges in cloud computing, paving the way for more efficient and reliable cloud services.

Page(s): 270-282

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

Keywords:

machine learning , Cloud Computing , throughput , Quality of Service , Response Time

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