AI-Driven Decision Support Systems for Green Management Cost Analysis: Optimizing Resource Allocation in Virtual Computer Systems

Abstract

The increased need for computation requires proper resource management particularly in managing virtual computer systems. This research aims to examine the implementation of the AI-driven Decision Support System (DSS) in relation to resource management for the most effective and efficient resource utilisation for cost optimisation with emphasis on environmental impacts. Built with machine learning algorithms especially reinforcement learning, the DSS adapts resource allocation in real-time use thus enhancing resource utilisation and reducing energy usage. The empirical analysis of the study presents a marked improvement in the average CPU usage from forty-five percent to sixty-five percent and memory usage from fifty percent to seventy percent. Also, the level of effectiveness was reached 33 in terms of DSS importance among the employees. This means that it can lead to up to 3% decrease in energy use and up to 25% decrease in operational expenses. These improvements support the suggestion that the system can efficiently allocate resources based on dynamic needs in order to minimise waste. By focusing on its applicability to green IT practices, the established results point to a future associated with the constant optimization of AI-driven DSS. Due to the inherent flexibility and the ability to transfer the DSS to different virtual structures, it is highly qualified for usage by IT managers and decision-makers. This research helps further the literature on sustainable IT management and showcases how AI can help change the face of sustainable computing for various virtual computer systems for the better.