Autonomous Cost Optimization in Multi-Cloud Environments Using AI-Driven Observability Frameworks
Keywords:
Multi-Cloud Computing, Cost Optimization, AI-Driven Observability, Reinforcement Learning, Predictive AnalyticsAbstract
The multi cloud architecture has been adopted at an unprecedented rate, resulting in immense challenges in cost management, resource allocation and operational visibility. For organizations, managing multiple cloud platforms can make for fragmented monitoring systems, unpredictable billing models, and wasted resources. This paper presents an autonomous cost optimization approach that uses AI-based observability to improve financial reliability and operational efficiency in multi-cloud environments. By integrating real-time telemetry data — logs, metrics, and traces — with sophisticated machine learning models that predict workload behavior, detect anomalies, and recommend cost-optimized actions. Using reinforcement learning and predictive analytics, this system would dynamically provision resources, allocate workloads efficiently, and remove unused or poorly used resources. Furthermore, the suggested methodology integrates explainable AI techniques to maintain transparency in decision-making, allowing stakeholders to comprehend optimization approaches and fostering confidence in automated systems. The experimental results show a significantly lower cost, better resources usage, and improved performance of the system over classical rule-based approaches. This architecture-based framework reduces operational costs and increases scalability, resiliency, and sustainability in multi-cloud deployments. This paper contributes to the emerging area of smart cloud management by proposing a scalable, on-demand, and data-driven solution for autonomous cost optimization.
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