Published Paper Details:

This study explores integrating artificial intelligence (AI) and machine learning (ML) techniques into cloud automation processes, focusing on enhancing auto-scaling mechanisms. Auto-scaling is a critical cloud management component, dynamically adjusting resources to meet fluctuating demands. Traditional auto-scaling methods often rely on static thresholds and reactive policies, which can lead to inefficiencies such as over-provisioning or resource shortages. This research addresses these limitations by employing predictive analytics and machine learning algorithms to create a more adaptive, intelligent, and proactive auto-scaling system. The research utilizes a combination of supervised and unsupervised machine learning models to predict workload patterns and optimize resource allocation in real time. Historical data from cloud infrastructure, including CPU usage, memory consumption, and network traffic, are analyzed to train these models. The study implements various algorithms, such as decision trees, neural networks, and reinforcement learning, to enhance the auto-scaling mechanisms' predictive accuracy and decision-making capabilities. A simulated cloud environment tests and validates the proposed system, ensuring its robustness and scalability. The findings demonstrate that AI-driven auto-scaling mechanisms significantly outperform traditional methods regarding resource utilization, cost efficiency, and response time. The predictive models successfully anticipate workload surges and optimize resource allocation before bottlenecks occur, leading to a smoother and more efficient cloud operation. Additionally, integrating machine learning into the auto-scaling process reduces the reliance on manual configurations and static policies, allowing for more dynamic and flexible cloud management. The implications of this research are far-reaching for cloud service providers and enterprises relying on cloud infrastructure. By leveraging AI and machine learning, organizations can achieve more efficient resource management, leading to cost savings, enhanced performance, and improved user experiences. The study also sets the stage for future advancements in cloud automation, where AI-driven approaches could become the norm, further pushing the boundaries of what cloud computing can achieve. This study discusses the role and capability of AI and machine learning in scaling clouds, focusing on improving auto-scaling dynamic attributes. Therefore, the switch from reactive resource management to predictive and proactive resource management is a welcome publication that provides new angles for increasing the smartness of cloud structures to meet the continuously growing demand.