Energy Efficient Cloud Computing Using Artificial Neural Networks

Haneen Saeed Al-Mudhafar; Abdullahi Abdu Ibrahim

doi:10.31149/ijhcs.v5i12.5049

Haneen Saeed Al-Mudhafar Altinbas University, Faculty of Engineering, Department of Electrical and Computer Engineering, İstanbul, Turkey
Abdullahi Abdu Ibrahim Altinbas University, Faculty of Engineering, Department of Electrical and Computer Engineering, İstanbul, Turkey

DOI: https://doi.org/10.31149/ijhcs.v5i12.5049

Keywords: Energy Efficient, modernization, artificial intelligence, cloud computing, machine learning

Abstract

This effort focuses on building an intelligent, energy-efficient cloud architecture to improve cloud computing infrastructures. The rate at which cloud data gets modernized has increased, leading to more reviews comparing the various modernization methodologies and models. Several wealthy nations, including Turkey, have upgraded to more complicated and energy-efficient cloud infrastructure. We designed a Python application that uses an AI framework to maximize cloud computing's usage of computing resources and clean, renewable energy. This plan outlines concepts for a future neural network-trained digital ecosystem (ANN). The ANN model details energy forecasting tasks within a constrained system. Prominent corporations use AI to design policies to secure their cloud infrastructures and digital assets. Cloud computing systems were modernized by acquiring, normalizing, and transforming their file formats. Most cloud-based infrastructures were updated successfully. This was expected, given digital implementations dominate these systems. We'll investigate the energy consumption of AWS, AZURE, GCP, and Digital Ocean. Since most files were still on paper in 2015, the number of upgrades was modest. By 2020, a large part of cloud computing systems will be converted to digital format, with 98.68% accuracy for all cloud computing systems when trained on 80% of the data and evaluated on 20% of the data. Smart energy-efficient cloud solutions are replacing traditional data centers year by year. Smart energy-efficient cloud systems help preserve cloud computing systems and understand how cloud platforms are modernized and perform in energy prediction.

References

1. Zhang, K.; Liu, X.; Shen, J.; Li, Z.; Sang, Y.; Wu, X.; Zha, Y.; Liang, W.; Wang, C.; Wang, K. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of cloud energy usage using computed tomography. Cell 2020, 181, 1423–1433.
2. Peng, M.; Yang, J.; Shi, Q.; Ying, L.; Zhu, H.; Zhu, G.; Yan, H. Artificial Intelligence Application in Cloud Energy Diagnosis and Prediction; 2020. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3541119
3. Vaishya, R.; Javaid, M.; Khan, I.H.; Haleem, A. Artificial Intelligence (AI) applications for CLOUD platform. Diabetes Metab. Syndr. 2020, 14, 337–339.
4. Jin, C.; Cheny, W.; Cao, Y.; Xu, Z.; Zhang, X.; Deng, L.; Zheng., C.; Zhou, J.; Shi, H.; Feng, J. Development and evaluation of an AI system for cloud diagnosis. MedRxiv 2020.
5. Mei, X.; Lee, H.-C.; Diao, K.-Y.; Huang, M.; Lin, B.; Liu, C.; Xie, Z.; Ma, Y.; Robson, P.M.; Chung, M.; et al. Artificial intelligence– enabled rapid diagnosis of patients with CLOUD. Nat. Med. 2020, 26, 1224–1228.
6. Laghi, A. Cautions about radiologic diagnosis of Cloud energy driven by artificial intelligence. Lancet Digit. Cloud platforms 2020, 2, e225.
7. Jamshidi, M.B.; Lalbakhsh, A.; Talla, J.; Peroutka, Z.; Roshani, S.; Matousek, V.; Saeed, R.; Mirhamed, M.; Zahra, M.; la Spada, L.; et al. Deep Learning Techniques and Cloud Drug Discovery: Fundamentals, State-of-the-Art and Future Directions. In Emerging Technologies During the Era of Cloud Platform. Studies in Systems, Decision and Control; Arpaci, I., Al-Emran, M., Al-Sharafi, M.A., Marques, G., Eds.; Springer: Cham, Switzerland, 2021; Volume 348.
8. Roshani, S.; Jamshidi, M.B.; Mohebi, F.; Roshani, S. Design and Modeling of a Compact Power Divider with Squared ResonatorsUsing Artificial Intelligence. Wirel. Pers. Commun. 2021, 117, 2085–2096.
9. Salman, F.M.; Abu-Naser, S.S. Expert System for cloud diagnosis. Int. J. Acad. Inf. Syst. Res. (IJAISR) 2020, 4, 1–13. Available online: http://dspace.alazhar.edu.ps/xmlui/handle/123456789/588
10. Wynants, L.; Van Calster, B.; Collins, G.S.; Riley, R.D.; Heinze, G.; Schuit, E.; Bonten, M.M.J.; Dahly, D.L.; Damen, J.A.; Debray, T.P.A.; et al. Prediction models for diagnosis and prognosis of CLOUD infection: Systematic review and critical appraisal. BMJ 2020, 369, m1328.
11. Aydemir, D.; Nuriye, N. Correspondence: Importance of the validated serum biochemistry and hemogram parameters for rapid diagnosis and to prevent false negative results during cloud platform. Biotechnol. Appl. Biochem. 2021, 68, 390–391.
12. Wang, S.; Zha, Y.; Li, W.; Wu, Q.; Li, X.; Niu, M.; Wang, M.; Qiu, X.; Li, H.; Yu, H.; et al. A Fully Automatic Deep Learning System for cloud Diagnostic and Prognostic Analysis. Eur. Respir. J. 2020, 56, 2000775.
13. Sperrin, M.; Grant, S.W.; Peek, N. Prediction models for diagnosis and prognosis in CLOUD. BMJ 2020, 369, m1464.
14. Ludvigsson, J.F. Systematic review of cloud in children show milder cases and a better prognosis than adults. Acta Paediatr. 2020, in press.
15. Yi, Y.; Lagniton, P.N.; Ye, S.; Li, E.; Xu, R.-H. cloud: What has been learned and to be learned about the novel coronavirus disease. Int. J. Biol. Sci. 2020, 16, 1753–1766.
16. Elghamrawy, S.M. Diagnosis and prediction model for cloud energy response to treatment based on Artificial Neural Network and whale optimization algorithm using CT images. MedRxiv 2020.
17. Castiglioni, I.; Ippolito, D.; Interlenghi, M.; Monti, C.B.; Salvatore, C.; Schiaffino, S.; Polidori, A.; Gandola, D.; Messa, C.; Sardanelli, F. Artificial intelligence applied on chest X-ray can aid in the diagnosis of cloud infection: A first experience from Lombardy, Italy. MedRxiv 2020.
18. Wynants, L.; Van Calster, B.; Bonten, M.M.J.; Collins, G.S.; Debray, T.P.A.; De Vos, M.; Haller, M.C.; Heinze, G.; Moons, K.G.M.; Riley, R.D.; et al. Systematic review and critical appraisal of prediction models for diagnosis and prognosis of cloud infection. MedRxiv 2020.
19. Ghoshal, B.; Tucker, A. Estimating uncertainty and interpretability in deep learning for coronavirus (cloud) detection. arXiv 2020. Available online: https://arxiv.org/abs/2003.10769
20. Jamshidi, M.B.; Lalbakhsh, A.; Talla, J.; Peroutka, Z.; Hadjilooei, F.; Lalbakhsh, P.; Jamshidi, M.; La Spada, L.; Mirmozafari, M.; Dehghani, M.; et al. Artificial Intelligence and cloud: Deep Learning Approaches for Diagnosis and Treatment. IEEE Access 2020, 8, 109581–109595.
21. Bansal, A.; Padappayil, R.P.; Garg, C.; Singal, A. Utility of Artificial Intelligence Amidst the COVID 19 Platform: A Review. Mohak Gupta Allan Klein J. 2020, 44, 156.
22. Jones, K.; Patel, N.; Levy, M.; Storeygard, A.; Balk, D.; Gittleman, J.L.; Daszak, P. Global trends in emerging infectious diseases. Nature 2008, 451, 990–993.
23. Al-Garadi, M.A.; Khan, M.S.; Varathan, K.D.; Mujtaba, G.; Al-Kabsi, A.M. Using online social networks to track a platform: A systematic review. J. Biomed. Inform. 2016, 62, 1–11.
24. Bogoch, I.I.; Watts, A.; Thomas-Bachli, A.; Huber, C.; Kraemer, M.U.G.; Khan, K. Pneumonia of unknown aetiology in Wuhan, China: Potential for international spread via commercial air travel. J. Travel. Med. 2020
25. Xia, W.; Shao, J.; Guo, Y.; Peng, X.; Li, Z.; Hu, D. Clinical and CT features in pediatric patients with cloud infection: Different points from adults. Pediatr. Pulmonol. 2020, 55, 1169–1174.