Enhancing Cloud Scalability with AI-Driven Resource Management
Keywords:
Cloud scalability, AI-driven Resource Management, Machine Learning Algorithms, Reinforcement Learning, Deep Q-Learning, Long Short-Term Memory Networks, LSTM Forecasting, Gradient Boosting Machines, XGBoostAbstract
This research paper aims at analyzing the factors that can help improve scalability of cloud by incorporating different machine learning algorithms in management of resources. Since controlling and managing cloud resources is becoming more challenging with compounded base requirements, the majority of conventional resource management solutions may not prove adequate. This research assesses the performance of five state-of-art machine learning techniques namely Reinforcement Learning, Long Short-Term Memory, Gradient Boosting Machines, Autoencoders and Neural Architecture Search in minimizing operational cost and enhancing resource utilization and overall system efficiency for improving business outcomes. The findings reveal that the use of RL-based approaches to optimize operational cost reduction and minimizing provisioning delay by 20% and 30% respectively and LSTM network to increase the accuracy of demand forecasting by 12% and overall efficiency of resource utilization by 22%. The use of GBM models in forecasts results in 30% error reduction in costs that drop by 20% while service improves by 25%. Using autoencoders, the models achieve 97% accuracy in detecting anomalies and infinityai1411@gmail.com in turn increasing the efficiency of allocation by 15 percent. The NAS-optimized models yield increased accuracy by a percentage point of 18 % as well as a 25% faster computational speed. Altogether, these theoretical developments demonstrate the ability of AI-based methodologies to enhance the cloud scalability promising and provide practical recommendations for improving resource management approaches in the cloud environment.
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