Machine Learning-Powered Monitoring Systems for Improved Data Reliability in Cloud Environments - Pentyala, Dillep Kumar (International Journal of Scientific Research and Management (IJSRM), 2012)
Cloud computing has become the backbone of modern digital infrastructure, enabling businesses to leverage scalable, on-demand resources for storage, computation, and data management. However, the dynamic nature of cloud environments introduces challenges in maintaining data reliability, a critical factor for ensuring the seamless operation of applications and services. Traditional monitoring systems, which rely on predefined thresholds and static rules, are often inadequate for detecting complex anomalies or predicting potential system failures in real-time.Machine learning (ML) offers a transformative approach to monitoring cloud environments, leveraging its ability to analyze vast amounts of data, identify patterns, and make accurate predictions. ML-powered monitoring systems dynamically adapt to changing workloads and conditions, enabling early detection of anomalies, predictive maintenance, and performance optimization. These systems utilize advanced algorithms such as neural networks, clustering, and decision trees to provide actionable insights that enhance system reliability and minimize downtime.This article explores the architecture, key components, and applications of machine learning-powered monitoring systems in cloud environments. It examines how ML can address challenges such as false positives, scalability, and evolving workloads. Real-world use cases, including anomaly detection, resource optimization, and security monitoring, are discussed to illustrate the practical benefits of these systems. Despite their promise, ML-powered systems face challenges such as high computational requirements, data privacy concerns, and the need for explainable AI to build trust in decision-making processes.Finally, the article outlines emerging trends in the field, including the integration of federated learning and edge computing to create more robust, decentralized monitoring systems. As organizations continue to embrace cloud technologies, adopting machine learning-powered monitoring systems will be crucial for achieving data reliability, enhancing performance, and maintaining competitive advantage in the digital age.