Estimating Cloud Performance Metrics Using Deep Learning

Abstract— Cloud computing has become the backbone of modern digital services, supporting applications that demand high availability, scalability, and performance. As cloud infrastructures grow in complexity, accurately estimating cloud performance metrics—such as response time, throughput, latency, resource utilization, and availability—has become a critical challenge. Traditional analytical and rule-based models often struggle to capture the dynamic, non-linear behavior of cloud environments. In this context, deep learning (DL) has emerged as a powerful data-driven approach for modeling and predicting cloud performance with higher accuracy and adaptability. A cloud environment is inherently dynamic due to factors such as fluctuating workloads, heterogeneous virtual machines, multi-tenancy, and varying network conditions. Performance metrics are influenced by complex interactions between compute, storage, and network resources. This paper presents a deep learning model for estimating cloud performance metrics. It can be observed that the proposed work attains improved performance compared to existing work in the domain. Keywords— Cloud Computing, Performance Estimation, service-level agreements (SLAs). Regression Learning, Deep Learning, Forecasting Accuracy.