AutoCloud AI: Reinventing Cloud Architecture with Intelligent Resource Optimization
1st Gudimella Padma Srivaishnavi 2nd Pathivada Sravanthi
Dept. Computer Application, Aditya University, Surampalem, India gudimellavaishnavi@gmail.com sravanthipathivada67@gmail.com
3rd Keerthi Kusuma,4th Koppu Durga Prasad,5 th Y. Gopal Krishna
Dept. Computer Application, Aditya University, Surampalem, India
kusumakeerthi2004@gmail.com,koppudurgaprasad813@gmail.com,ygopalkrishna443@gmail.com
Abstract—Cloud computing emerged as the foundation of the new digital infrastructure, and the classical methods of managing resources are not able to cope with the unpredictable dynamic character of the new workloads. In this paper, AutoCloud AI is a new intelligent cloud architecture based on sophisticated artificial intelligence and machine learning methods used to transform resource optimization. AutoCloud AI overcomes the presence of reactive, workload variability, multi-objective tradeoff and operational complexity shortcomings in existing cloud management systems with a hybrid control pipeline that combines predictive forecasting, reinforcement learning-based policy implementation, spot-risk awareness, and explainable AI policies. We generalize the experience of the recent developments in AI-based cloud resource management and discuss the approaches using both deep learning-based forecasting models (LSTM, CNN, Transformer) and reinforcement learning-based control mechanisms and techniques of the ensemble selection. We find out that the current methods are able to deliver decent results in the context of cost reduction (25-40%), the resource utilization (20-45%), and optimization of response time (35-44), but exhibited large gaps in production deployment, multi-objective optimization, and operational integration. AutoCloud AI suggests a multifaceted architecture where time-series forecast demand prediction, adaptive multi-forecast selection, scaling policy based on RL, spot-instance risk classification, edge-aware placement and explainable decision-making develop a self-optimizing production cloud infrastructure. The study provides not only a critical description of the state-of-the-art in intelligent cloud resource management but also a speculative vision of the architectural structure of autonomous, efficient, and resilient cloud systems.
Keywords: Cloud Computing, Resource Optimization, Artificial Intelligence, Machine Learning, Auto-scaling, Reinforcement Learning, Predictive Analytics, Cloud Architecture.
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