Authors: Priya Narayanan
Abstract: Neural network optimization has become a critical driver in advancing real-time cloud decision systems, fundamentally transforming how cloud resources and workloads are managed dynamically and efficiently. As cloud computing infrastructures grow in complexity and scale, neural networks—especially deep learning models—offer powerful capabilities to process vast amounts of data, detect intricate patterns, and predict future states of cloud environments with high accuracy. These capabilities enable cloud platforms to allocate resources, balance loads, and automate decision-making in real-time, thus improving performance, reducing latency, enhancing cost-effectiveness, and boosting energy efficiency. This article explores the multifaceted impact of neural network optimization on cloud decision systems, examining key techniques such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), Bayesian neural networks (BNNs), and graph neural networks (GNNs). It discusses the integration of these models in workload forecasting, resource allocation, and system adaptability, highlighting their role in enabling cloud environments to respond proactively to changing demands. Furthermore, the analysis covers challenges such as model interpretability, real-time processing constraints, and scalability. The article concludes with insights on emerging trends and future directions, emphasizing how neural network optimization will continue to shape the agility and intelligence of cloud decision-making frameworks.
DOI: http://doi.org/10.5281/zenodo.17777606
