Intelligent Cloud Resource Management Integrating Machine Learning with Observability Tools for Cost and Performance Optimization
Keywords:
Cloud Resource Management, Machine Learning, Observability Tools, Cost Optimization, Auto-Scaling.Abstract
Modern cloud computing environments demand dynamic, intelligent resource allocation strategies capable of adapting to fluctuating workloads while minimizing operational expenditure. This paper presents a comprehensive framework for intelligent cloud resource management by integrating machine learning (ML) algorithms with advanced observability tools to achieve simultaneous cost and performance optimization. The proposed system leverages real-time telemetry data — encompassing metrics, logs, and distributed traces — collected through observability platforms such as Prometheus, Grafana, and OpenTelemetry, which are subsequently processed by predictive ML models including Long Short-Term Memory (LSTM) networks and reinforcement learning agents. These models enable proactive auto-scaling, anomaly detection, and workload forecasting, significantly reducing over-provisioning and under-utilization of cloud resources. Experimental evaluations conducted across multi-cloud and hybrid environments demonstrate that the integrated framework achieves up to 35% reduction in infrastructure costs while maintaining service-level agreement (SLA) compliance exceeding 99.5%. Furthermore, the system exhibits adaptive behavior under sudden traffic spikes, outperforming conventional threshold-based autoscaling mechanisms. The findings underscore the transformative potential of combining ML-driven intelligence with full-stack observability, establishing a scalable and robust foundation for next-generation cloud resource governance in enterprise-grade deployments.
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