AN ENHANCED ENTERPRISE METHOD FOR CLOUD LOAD BALANCING  FORECASTING USING DEEP CNN

Dr. B. Phijik, Dr. G. Rajesh, Samreen Begum, Dr. T. Srinivasulu

AN ENHANCED ENTERPRISE METHOD FOR CLOUD LOAD BALANCING FORECASTING USING DEEP CNN

Authors

Dr. B. Phijik, Dr. G. Rajesh, Samreen Begum, Dr. T. Srinivasulu

Keywords:

Cloud EC2, Load Balancing, Machine Learning, Deep CNN, Support Vector Machine

Abstract

In the modern era of digital transformation, cloud computing has emerged as a fundamental technologyfor enterprises, offering scalable, on-demand resources to meet diverse computational and service oriented needs. However,

References

B. Feng, Z. Ding and C. Jiang, "FAST: A Forecasting Model With Adaptive Sliding Window and Time Locality Integration for Dynamic Cloud Workloads," in IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 1184-1197, 1 March-April 2023, doi: 10.1109/TSC.2022.3156619.

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Published

2024-12-20

How to Cite

Dr. B. Phijik, Dr. G. Rajesh, Samreen Begum, Dr. T. Srinivasulu. (2024). AN ENHANCED ENTERPRISE METHOD FOR CLOUD LOAD BALANCING FORECASTING USING DEEP CNN. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7037–7045. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4096