AI-Driven Predictive Auto-Scaling for Cloud-Native Systems with Real-Time Anomaly Detection
Keywords:
AI-driven auto-scaling, cloud observability, anomaly detection, predictive scaling, AWS infrastructure, time-series forecasting, self-healing automation.Abstract
Cloud-native architectures demand highly dynamic resource scaling to handle fluctuating workloads efficiently. Traditional reactive scaling methods often lead to over-provisioning, under-utilization, or performance degradation. This paper introduces an AI-driven predictive auto-scaling framework that leverages machine learning-based observability data to anticipate resource demand proactively. By integrating real-time anomaly detection, this approach minimizes system failures due to unexpected surges or resource misallocations. Our proposed solution utilizes Long Short-Term Memory (LSTM) networks for predictive analytics and an unsupervised anomaly detection model to optimize AWS-based cloud infrastructures. Experimental results demonstrate improved cost efficiency, reduced latency, and enhanced system resilience, outperforming conventional auto-scaling mechanisms.
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