AI and Cloud Service Quality: Predictive Analytics for Performance Monitoring and Enhancement
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P101Keywords:
AI-driven analytics, Predictive maintenance, Cloud computing, Machine learning, Data-driven Decisionmaking, Industrial automation, Real-time monitoring, Process optimization, Anomaly detection, Smart manufacturingAbstract
The rapid advancement of cloud computing has revolutionized the way businesses and organizations operate, providing scalable, flexible, and cost-effective solutions. However, the dynamic and complex nature of cloud environments poses significant challenges in maintaining high service quality. This paper explores the application of artificial intelligence (AI) and predictive analytics in enhancing cloud service quality through performance monitoring and proactive management. We discuss the theoretical foundations, key methodologies, and practical applications of AIdriven predictive analytics in cloud computing. The paper also presents case studies and empirical evidence to demonstrate the effectiveness of these techniques in improving service reliability, reducing downtime, and optimizing resource utilization. Finally, we discuss future research directions and the potential impact of AI and predictive analytics on the cloud computing industr
References
[1] Algomox. (2022, July 15). Role of AI in predictive analytics for proactive service management in managed cloud services. Algomox. https://www.algomox.com/resources/blog/ai_predictive_analytics_managed_cloud.html
[2] Brainvire. (2020, September 10). AI in cloud computing is bringing efficiency and scalability. Brainvire. https://www.brainvire.com/blog/driving-efficiency-by-harnessing-ai-for-cloud-optimization/
[3] Gill, N. S. (2024, November 12). AI-powered predictive maintenance for cloud operations. XenonStack. https://www.xenonstack.com/blog/ai-maintenance-cloud-operations
[4] Mungoli, N. (2020, April 26). Scalable, distributed AI frameworks: Leveraging cloud computing for enhanced deep learning performance and efficiency. arXiv. https://arxiv.org/abs/2304.13738
[5] New Relic. (2021, September 1). AI in observability: Advancing system monitoring and performance. New Relic. https://newrelic.com/blog/how-to-relic/ai-in-observability
[6] Shi, T., Yang, Y., Cheng, Y., Gao, X., Fang, Z., & Yang, Y. (2023, July 18). Alioth: A machine learning based interference-aware performance monitor for multi-tenancy applications in public cloud. arXiv. https://arxiv.org/abs/2307.08949
[7] Splunk. (2019, August 20). Service performance monitoring explained. Splunk. https://www.splunk.com/en_us/blog/learn/service-performance-monitoring.html
[8] Srinivas, P., Husain, F., Parayil, A., Choure, A., Bansal, C., & Rajmohan, S. (2009, February 29). Intelligent monitoring framework for cloud services: A data-driven approach. arXiv. https://arxiv.org/abs/2403.07927
[9] To The New. (2015, October 5). AI-driven cloud monitoring: A new frontier for business efficiency and cost optimization. To The New. https://www.tothenew.com/blog/ai-driven-cloud-monitoring-a-new-frontier-for-business-efficiency-and-cost-optimization/
[10] XenonStack. (2015, November 12). AI-powered predictive maintenance for cloud operations. XenonStack. https://www.xenonstack.com/blog/ai-maintenance-cloud-operations
