Machine Learning Applications for Predictive Analytics in Cloud Computing Systems
Keywords:
Machine Learning, Predictive Analytics, Cloud Computing, Anomaly Detection, Neural Networks, Data PrivacyAbstract
The integration of machine learning (ML) into cloud computing systems has transformed predictive analytics, enabling advanced decision-making and efficient resource allocation. This paper explores key applications of ML for predictive analytics in cloud environments, focusing on its role in anomaly detection, workload optimization, and predictive maintenance. By leveraging algorithms such as decision trees, neural networks, and reinforcement learning, cloud platforms can improve system reliability, reduce operational costs, and enhance scalability. The paper also addresses challenges related to data privacy, computational efficiency, and the interoperability of ML models across diverse cloud architectures. The findings emphasize the potential of ML to reshape cloud-based analytics, driving innovation in service delivery and operational excellence.
References
1. Kang, J., Kim, H., and Choi, S. "Scalable Anomaly Detection in Cloud Environments Using Deep Learning Models." IEEE Transactions on Cloud Computing, vol. 8, no. 3, 2020, pp. 545–556.
2. Wang, Y., and Zhao, X. "Predictive Maintenance Framework Using Machine Learning for Cloud-Based Systems." Journal of Cloud Computing, vol. 7, no. 4, 2019, pp. 78–89.
3. Gupta, P., and Singh, R. "Neural Network-Based Workload Prediction for Optimizing Cloud Resource Allocation." Journal of Supercomputing, vol. 76, no. 2, 2020, pp. 1585–1601.
4. Chen, T., and Li, Y. "A Hybrid Machine Learning Approach for Cloud System Fault Prediction." Future Generation Computer Systems, vol. 85, no. 1, 2018, pp. 169–177.
5. Zhao, L., and Hu, J. "Efficient Predictive Analytics in the Cloud Using Distributed Machine Learning Algorithms." ACM Transactions on Internet Technology, vol. 19, no. 4, 2019, Article 52.
6. Patel, D., and Shah, R. "Privacy-Preserving Machine Learning Models for Predictive Analytics in Cloud." IEEE Security & Privacy, vol. 18, no. 3, 2020, pp. 45–52.
7. Singh, A., and Verma, S. "Dynamic Resource Allocation in Cloud Systems Using Reinforcement Learning." International Journal of Advanced Computer Science and Applications, vol. 11, no. 4, 2020, pp. 83–90.
8. Kaur, M., and Aggarwal, S. "Implementation of Machine Learning Algorithms for Cloud Security Analytics." Cluster Computing, vol. 22, no. 6, 2019, pp. 15675–15685.
9. Liu, Q., Zhang, P., and Yu, W. "Predictive Cloud Analytics Using Deep Learning Techniques for Smart City Applications." IEEE Transactions on Smart Grid, vol. 9, no. 2, 2018, pp. 1010–1022.
10. Rahman, M., and Alam, M. "Optimization of Big Data Workflows in Cloud Systems Using Machine Learning." Future Internet, vol. 11, no. 12, 2019, p. 332.
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Copyright (c) -1 Zaidoon Mohamad Abdulrazzaq (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
