Journal of Soft Computing Paradigm
IRO Journals
Volume 3 — Issue 3 — September 2021

Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

https://doi.org/10.36548/jscp.2021.3.008
N. Bhalaji
Associate Professor, Department of Information Technology, SSN College of Engineering, Kalavakkam, Tamil Nadu, India
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How to Cite

Bhalaji, N. 2021. “Cloud Load Estimation With Deep Logarithmic Network for Workload and Time Series Optimization”. Journal of Soft Computing Paradigm 3 (3): 234-48. https://doi.org/10.36548/jscp.2021.3.008.
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Keywords

— Workload
— Time Series
— Logarithmic Operation
— Deep Learning
— Min-Max Scalar
Published: 27-09-2021

Abstract

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

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