Artificial Intelligence Integrated Cloud Resource Management Model for Sustainable High Performance Enterprise Computing
DOI:
https://doi.org/10.15680/x2p64553Keywords:
Artificial Intelligence, Cloud Computing, Resource Management, High-Performance Computing, Sustainable Computing, Predictive Analytics, Machine Learning, Deep Learning, Reinforcement Learning, Enterprise ITAbstract
The growing adoption of cloud computing in enterprise environments has transformed the way organizations manage computing resources, enabling scalable, high-performance, and flexible IT infrastructures. However, the dynamic and heterogeneous nature of cloud environments presents challenges in resource allocation, energy efficiency, workload scheduling, cost optimization, and sustainability. This study proposes an artificial intelligence integrated cloud resource management model designed to optimize performance, reduce energy consumption, and enhance sustainability for enterprise computing systems. By leveraging machine learning, deep learning, and reinforcement learning algorithms, the model enables intelligent monitoring, predictive workload allocation, dynamic scaling of resources, and proactive fault management. Experimental simulations demonstrate that AI-driven resource management improves server utilization, reduces energy consumption by up to 25%, minimizes service-level agreement violations, and enhances overall system throughput. Additionally, the model incorporates predictive analytics for demand forecasting and anomaly detection, allowing proactive mitigation of resource bottlenecks and performance degradation. This research contributes to the development of sustainable high-performance computing architectures that balance operational efficiency, cost-effectiveness, and environmental responsibility, providing enterprises with a robust framework for AI-enabled cloud resource optimization and intelligent decision-making in dynamic IT environments.
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