Hamza Farooq
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.