Resource Optimization in Cloud Computing Using Big Data Analytics

  • Authors

    • Dr. Lydia Languish School of Business, University of Sydney, Australia. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2019PI8N4J

    Published 04-03-2019

  • Cloud Computing, Resource Optimization, Big Data Analytics, Predictive Analytics, Machine Learning, Resource Allocation, Cloud Resource Management, Data Centers, Scalability, Energy Efficiency

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    L. Languish, “Resource Optimization in Cloud Computing Using Big Data Analytics”, IJADSMC, vol. 2, no. 1, pp. 01–15, Apr. 2019, doi: 10.67228/30713498/IJADSMC-2019PI8N4J.

  • Abstract

    Cloud computing has revolutionized the way businesses and individuals access computing resources by offering on-demand services in a scalable and flexible manner. However, managing and optimizing cloud resources efficiently remains a significant challenge, particularly as cloud environments grow in complexity. The integration of big data analytics into cloud computing offers a promising solution for improving resource optimization. This paper explores how big data analytics can be leveraged to enhance resource allocation, reduce operational costs, and improve system performance in cloud computing environments. It discusses various techniques such as predictive analytics, machine learning models, and data-driven decision-making algorithms that contribute to efficient cloud resource management. Additionally, the paper highlights real-world applications and case studies demonstrating the successful implementation of these techniques. Challenges and limitations, such as data privacy concerns and the scalability of analytics frameworks, are also discussed. Finally, the paper outlines future research directions and opportunities in the field of cloud computing resource optimization through big data analytics.

  • References

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