Development of Naïve AI-based Model for Resource Management in Cloud Computing

Abstract

Cloud computing has become a cornerstone of modern IT infrastructure, enabling scalable, on-demand access to computing resources. However, efficient resource management remains a critical challenge due to dynamic workloads, varying user demands, and the complexity of resource allocation. This paper presents the development of a naïve Artificial Intelligence (AI)-based model for resource management in cloud computing environments. The proposed model leverages basic machine learning techniques to predict resource demand and automate allocation decisions with minimal overhead. Unlike complex AI frameworks, the naïve approach focuses on simplicity, interpretability, and ease of integration into existing cloud platforms. The model was trained using historical usage data and tested on simulated cloud workloads to evaluate its performance in terms of resource utilisation, response time, and cost efficiency. Initial results show that even a simple AI-driven model can significantly improve resource allocation compared to traditional static methods. This research highlights the potential of lightweight AI models in effectively managing cloud resources, paving the way for further optimisation using more advanced techniques.