Intelligent Auto-Scaling in AWS: Machine Learning Approaches for Predictive Resource Allocation
The deployment of intelligent auto-scaling solutions across the cloud environment simultaneously decreases the operational spend as well as distribute resources effectively. The research investigates the deployment of predictive auto-scaling with machine learning in Amazon Web Services (AWS) to improve system scalability as well as management efficiency and economical resource usage. The proposed system implements advanced ML algorithms to reach 92% prediction accuracy thus it minimizes scaling latency and optimizes resource utilization. Analysis reveals that ML-based approaches exceed threshold-based methods because they provide superior response times as well as reduced costs and maximum system availability. Performance evaluations with cost analysis reveal that predictive resource allocation has great future potential for cloud infrastructure management. The discovery demonstrates how ML-based auto-scaling creates a perfect solution for modern cloud challenges by uniting cost-saving measures with high scalability and efficiency benefitsIndependent Researcher
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