Resource utilization analytics dashboard for cloud infrastructure management

Effective management of resource utilization is essential for maintaining the performance, scalability, and cost efficiency of modern cloud infrastructures. As organizations increasingly adopt hybrid and multi-cloud environments, monitoring and optimizing distributed resources have become complex and data-intensive tasks. This paper presents the development of a Resource Utilization Analytics Dashboard (RUAD) designed to provide unified visibility and intelligent analytics across diverse cloud platforms. The proposed system integrates real-time data collection, machine-learning-based prediction, and anomaly detection to identify patterns of under- and over-utilization. Using time-series analysis and adaptive algorithms, the dashboard delivers proactive insights that enable dynamic workload balancing, cost optimization, and service-level improvement. The modular architecture allows seamless integration with major providers such as AWS, Azure, and Google Cloud, ensuring interoperability and scalability. A user-centric interface visualizes key metrics—CPU, memory, network, and storage utilization—through interactive charts and alerts. Experimental evaluations with real-world datasets demonstrate that the system can reduce idle resource costs by approximately 25% while sustaining 99.9% uptime reliability. Furthermore, predictive accuracy tests using ARIMA and LSTM models achieved less than 5% mean absolute error, confirming the system’s analytical robustness. Overall, RUAD offers a comprehensive and scalable framework for intelligent cloud resource management, contributing to the ongoing transformation toward autonomous and energy-efficient cloud operations.