APA Style
Mamidala, J. V. , Mamidala, J. V. Enokkaren, S. J. , Enokkaren, S. J. Attipalli, A. , Attipalli, A. Bitkuri, V. , Bitkuri, V. Kendyala, R. , & Kendyala, R. (2022). Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning.
Current Research in Public Health, 2(1), 165-175.
https://doi.org/10.31586/jaibd.2022.1344
ACS Style
Mamidala, J. V. ; Mamidala, J. V. Enokkaren, S. J. ; Enokkaren, S. J. Attipalli, A. ; Attipalli, A. Bitkuri, V. ; Bitkuri, V. Kendyala, R. ; Kendyala, R. Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning.
Current Research in Public Health 2022 2(1), 165-175.
https://doi.org/10.31586/jaibd.2022.1344
Chicago/Turabian Style
Mamidala, Jaya Vardhani, Jaya Vardhani Mamidala. Sunil Jacob Enokkaren, Sunil Jacob Enokkaren. Avinash Attipalli, Avinash Attipalli. Varun Bitkuri, Varun Bitkuri. Raghuvaran Kendyala, and Raghuvaran Kendyala. 2022. "Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning".
Current Research in Public Health 2, no. 1: 165-175.
https://doi.org/10.31586/jaibd.2022.1344
AMA Style
Mamidala JV, Mamidala JVEnokkaren SJ, Enokkaren SJAttipalli A, Attipalli ABitkuri V, Bitkuri VKendyala R, Kendyala R. Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning.
Current Research in Public Health. 2022; 2(1):165-175.
https://doi.org/10.31586/jaibd.2022.1344
@Article{crph1344,
AUTHOR = {Mamidala, Jaya Vardhani and Enokkaren, Sunil Jacob and Attipalli, Avinash and Bitkuri, Varun and Kendyala, Raghuvaran and Kurma, Jagan},
TITLE = {Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning},
JOURNAL = {Current Research in Public Health},
VOLUME = {2},
YEAR = {2022},
NUMBER = {1},
PAGES = {165-175},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1344},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2022.1344},
ABSTRACT = {In the constantly evolving world of cloud computing, appropriate resource allocation is essential for both keeping costs down and ensuring an ongoing flow of apps and services. Because of its adaptability to specific tasks and human behavior, machine learning (ML) is a desirable choice for fulfilling those needs. This study Efficient cloud resource allocation is critical for optimizing performance and cost in cloud computing environments. In order to improve the precision of resource allocation, this study investigates the use of Long Short-Term Memory (LSTM). The LSTM model achieved 97% accuracy, 97.5% precision, 98% recall, and a 97.8% F1-score (F1-score: harmonic mean of precision and recall), according to experimental data. The confusion matrix demonstrates strong classification performance across several resource classes, while the accuracy and loss curves verify steady learning with minimal overfitting. The suggested LSTM model performs better than more conventional ML (machine learning) models like Gradient Boosting (GB) and Logistic Regression (LR), according to a comparative study. These findings underscore the LSTM (Long Short-Term Memory) model’s robustness and suitability for dynamic cloud environments, enabling more accurate forecasting and efficient resource management.},
}
%0 Journal Article
%A Mamidala, Jaya Vardhani
%A Enokkaren, Sunil Jacob
%A Attipalli, Avinash
%A Bitkuri, Varun
%A Kendyala, Raghuvaran
%A Kurma, Jagan
%D 2022
%J Current Research in Public Health
%@ 2831-5162
%V 2
%N 1
%P 165-175
%T Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning
%M doi:10.31586/jaibd.2022.1344
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1344
TY - JOUR
AU - Mamidala, Jaya Vardhani
AU - Enokkaren, Sunil Jacob
AU - Attipalli, Avinash
AU - Bitkuri, Varun
AU - Kendyala, Raghuvaran
AU - Kurma, Jagan
TI - Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning
T2 - Current Research in Public Health
PY - 2022
VL - 2
IS - 1
SN - 2831-5162
SP - 165
EP - 175
UR - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1344
AB - In the constantly evolving world of cloud computing, appropriate resource allocation is essential for both keeping costs down and ensuring an ongoing flow of apps and services. Because of its adaptability to specific tasks and human behavior, machine learning (ML) is a desirable choice for fulfilling those needs. This study Efficient cloud resource allocation is critical for optimizing performance and cost in cloud computing environments. In order to improve the precision of resource allocation, this study investigates the use of Long Short-Term Memory (LSTM). The LSTM model achieved 97% accuracy, 97.5% precision, 98% recall, and a 97.8% F1-score (F1-score: harmonic mean of precision and recall), according to experimental data. The confusion matrix demonstrates strong classification performance across several resource classes, while the accuracy and loss curves verify steady learning with minimal overfitting. The suggested LSTM model performs better than more conventional ML (machine learning) models like Gradient Boosting (GB) and Logistic Regression (LR), according to a comparative study. These findings underscore the LSTM (Long Short-Term Memory) model’s robustness and suitability for dynamic cloud environments, enabling more accurate forecasting and efficient resource management.
DO - Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning
TI - 10.31586/jaibd.2022.1344
ER -
