AI-Based Monitoring Systems for Cloud Infrastructure
Authors-Amirul Hakim
Keyword-AI-Based Monitoring, Cloud Infrastructure, Machine Learning, Anomaly Detection, Predictive Analytics, Performance Optimization, Resource Management, Fault Detection, Cloud Reliability, Self-Adaptive Systems, Data Analytics, Automated Monitoring, Cloud Performance, Scalability, Real-Time Insights.
The rapid adoption of cloud computing has led to increasingly complex and dynamic infrastructure environments, making monitoring a critical aspect of operational efficiency, reliability, and security. AI-based monitoring systems leverage machine learning, anomaly detection, and predictive analytics to provide real-time insights into cloud infrastructure performance, resource utilization, and potential faults. This study explores the design, implementation, and benefits of AI-driven monitoring solutions for cloud environments. It examines techniques for automated data collection, performance analysis, anomaly detection, and predictive maintenance, highlighting their ability to reduce downtime, optimize resource allocation, and improve decision-making. Additionally, the research addresses challenges such as data heterogeneity, scalability, and model accuracy. By analyzing practical applications and industry case studies, this study demonstrates that AI-based monitoring is essential for achieving resilient, efficient, and self-adaptive cloud infrastructure.