AI-Powered Monitoring and Predictive Maintenance for Cloud Infrastructure: Leveraging AWS Cloud Watch and ML
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P107Keywords:
Cloud Computing, AI-Driven Monitoring, Predictive Maintenance, AWS Cloudwatch, Anomaly Detection, Machine Learning, System Reliability, Proactive Fault Prevention, AI In IT Operations, Cloud Infrastructure OptimizationAbstract
The IT infrastructure domain benefits from cloud computing because it delivers customizable resources available on demand. Creating reliable cloud system operations continues to be difficult because dynamic workload changes clash with unpredictable system failures and the intricate nature of distributed architectures. Monitoring methods relying on static thresholds together with rule-based alerts deliver reactive responses but they do not produce sufficient disruption prevention. The research investigates how AI facilitates predictive maintenance for cloud systems with the help of AWS CloudWatch combined with machine learning algorithms for advanced failure prediction and anomaly detection. This research introduces a framework that uses a combination of supervised and unsupervised ML models for AWS CloudWatch metrics and logs processing through Amazon SageMaker and AI analytics to deliver real-time monitoring and proactive fault prevention. The research shows how AI-enabled predictive maintenance cuts down both Mean Time to Detect (MTTD) and Mean Time to Repair (MTTR) leading to better resource use while decreasing service interruptions. Composite AI solutions alongside improved IoT integration and explainable AI systems are rising as potential solutions to overcome data quality, scalability issues and security concerns in AI monitoring. The next phase of investigation needs to prioritize improved computational precision and security to advance predictive maintenance methods for cloud services systems
References
[1] A. Ucar, M. Karakose, and N. Kırımça, "Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends," Applied Sciences, vol. 14, no. 2, p. 898, 2024. [Online]. Available: https:// www.mdpi.com/2076-3417/14/2/898
[2] "Predictive Maintenance Using Machine Learning," Amazon Web Services, 2024. [Online]. Available: https://www.amazonaws.cn/en/solutions/predictive-maintenance/
[3] "What is AIOps? - Artificial intelligence for IT Operations Explained," Amazon Web Services. [Online]. Available: https://aws.amazon.com/what-is/aiops/
[4] R. Munir, S. A. Khan, and A. Usman, "A Deep Learning Approach for Unsupervised Anomaly Detection in Time-Series," in 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France, 2019, pp. 861-866. [Online]. Available: https://ieeexplore.ieee.org/document/8924284
[5] S. Gupta, A. K. Somani, and A. K. Somani, "Failure Prediction Models for Cloud Applications," in 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA, 2017, pp. 312-319. [Online]. Available: https:// ieeexplore.ieee.org/document/8027552
[6] A. S. S. Gill, R. Buyya, and A. V. Dastjerdi, "A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View," in 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA, 2017, pp. 524-531. [Online]. Available: https://ieeexplore.ieee.org/document/8027559
[7] "Iberdrola y Amazon buscan potenciar el uso de la IA generativa en el sector energético," Cinco Días, Aug. 16, 2024. [Online]. Available: https:// cincodias.elpais.com/companias/2024-08-16/iberdrola-y-amazon-buscan-potenciar- el-uso-de-la-ia-generativa-en-el-sector-energetico.html
[8] "AWS CloudWatch AI-Powered Predictive Maintenance," Amazon Web Services. [Online]. Available: https://aws.amazon.com/about-aws/whats-new/2019/07/ introducing-predictive-maintenance-using-machine-learning/
[9] Y. Zhang, L. Wang, and J. Chen, "AI-Powered Cloud Infrastructure Monitoring: A Deep Learning Approach," IEEE Transactions on Cloud Computing, vol. 12, no. 3,
[10] pp. 225-238, 2024. [Online]. Available: https://ieeexplore.ieee.org/document/9856324
[11] M. Anderson, "Future Trends in AI-Driven Cloud Monitoring," Journal of Emerging Cloud Technologies, vol. 6, no. 2, pp. 78-91, 2024.
[12] ProSource, "The Role of AI in Predictive Maintenance for Data
[13] Centers," [Online]. Available: https://www.team-prosource.com/the-role-of-ai-in- predictive-maintenance-for-data-centers/. [Accessed: Jan. 29, 2025].
[14] Anunta Tech, "Revolutionizing IT Infrastructure: How AI Predictive Maintenance is Changing the Game," [Online]. Available: https://www.anuntatech.com/blog/ revolutionizing-it-infrastructure-how-ai-predictive-maintenance-is-changing-the- game/. [Accessed: Jan. 29, 2025].
[15] Deloitte, "Using AI in Predictive Maintenance," [Online]. Available: https:// www2.deloitte.com/us/en/pages/consulting/articles/using-ai-in-predictive- maintenance.html. [Accessed: Jan. 29, 2025].
[16] Oracle, "AI-Powered Predictive Maintenance: What You Need to
[17] Know," [Online]. Available: https://www.oracle.com/scm/ai-predictive-maintenance/. [Accessed: Jan. 29, 2025].
[18] XenonStack, "AI-Driven Predictive Maintenance for Cloud Operations," [Online]. Available: https://www.xenonstack.com/blog/ai-maintenance-cloud-operations. [Accessed: Jan. 29, 2025].
[19] Avantune, "Optimizing the IT Lifecycle: Predictive Maintenance and AI in Cloud Management," [Online]. Available: https://avantune.com/blogs/avantune-blog/posts/ 7481925/predictive-maintenance-and-artificial-intelligence-in-cloud-management- optimizing-the-it-lifecycle. [Accessed: Jan. 29, 2025].
[20] Rapid Innovation, "AI for Predictive Maintenance: Challenges and
[21] Solutions," [Online]. Available: https://www.rapidinnovation.io/post/ai-for-predictive- maintenance. [Accessed: Jan. 29, 2025].
[22] Forbes, "Harnessing the Power of AI at the Edge: Transforming Predictive Maintenance and Automation," [Online]. Available: https://www.forbes.com/councils/ forbestechcouncil/2024/08/12/harnessing-the-power-of-ai-at-the-edge-transforming- predictive-maintenance-and-automation/. [Accessed: Jan. 29, 2025].
[23] ArXiv, "Advancing Predictive Maintenance with Deep Learning Models," [Online]. Available: https://arxiv.org/abs/2404.13454. [Accessed: Jan. 29, 2025].
[24] M. Sipos, D. Fradkin, F. Moerchen, and Z. Wang, "Log-based predictive maintenance," in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp. 1867-1876.
[25] C. Zhang, Y. Chen, and D. Zhang, "Deep neural network for predictive maintenance in cloud manufacturing," International Journal of Advanced Manufacturing Technology, vol. 105, no. 9, pp. 4003-4012, 2019.
[26] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, 2016.
[27] Y. Lu, L. D. Xu, and N. Xu, "Development of a hybrid cloud computing framework for deploying cloud-based systems," IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1506-1513, 2014.
[28] J. Lee, H. A. Kao, and S. Yang, "Service innovation and smart analytics for industry 4.0 and big data environment," Procedia CIRP, vol. 16, pp. 3-8, 2014.
[29] K. Wang, Y. Wang, and Y. Chen, "A hybrid approach for dynamic predictive maintenance modeling and scheduling," IEEE Transactions on Reliability, vol. 65, no. 1, pp. 369-379, 2016.
[30] R. Daruvuri, K. Patibandla, and P. Mannem, "Leveraging unsupervised learning for workload balancing and resource utilization in cloud architectures," International Research Journal of Modernization in Engineering Technology and Science, vol. 6, no. 10, pp. 1776-1784, 2024.
