Open AccessCloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Author(s) -
N. Bhalaji
Publication year - 2021
Publication title -
journal of soft computing paradigm
Language(s) - English
Resource type - Journals
ISSN - 2582-2640
DOI - 10.36548/jscp.2021.3.008
Subject(s) - workload , computer science , cloud computing , series (stratigraphy) , time series , logarithm , deep learning , real time computing , data mining , artificial intelligence , machine learning , mathematics , paleontology , mathematical analysis , biology , operating system
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.