Autonomous Workload Right-Sizing for Multi-Cloud Cost Optimization

The rapid ascent of cloud computing has fundamentally changed the way enterprise IT works, thus allowing companies to quickly and easily spread their workloads over different cloud providers. But the truth is the price of this convenience has been quite high, in fact, it has been quite literally high as well. Cloud spending inefficiencies like underutilization and above normal provisioning standards together account for the annual loss of billions of dollars in cloud expenditure. Conventional right-sizing tactics are primarily dependent on fixed thresholds and labor-intensive processes, thus being unable to handle the variable and intricate nature of the workloads in multi-cloud setups.This paper puts forward the Autonomous Workload Right-Sizing Engine (AWRE), a framework that learns on its own and uses various cutting-edge technologies such as Reinforcement Learning (RL), Causal Inference Modeling, and a Policy-Aware Optimization Layer (PAOL) to mix the workload configurations among AWS, Azure, and Google Cloud in an intelligent and autonomous manner. AWRE not only tracks continuously the workload telemetry such as CPU utilization, memory usage, latency, and compliance status but also makes real-time adjustments to the configurations for the purpose of optimizing cost and performance simultaneously.The system utilizes Causal Impact Analysis to distinguish actual performance improvements from random fluctuations and incorporates a Graph Neural Network (GNN)-based Anomaly Suppression Module (ASM) for identifying and precluding unsafe optimization actions. AWRE which is operational in more than 10,000 enterprise workloads, accomplished 35% cut down of operational costs, 22% improvement of performance efficiency, and 15% increase in workload stability while still being completely in line with security and governance policies.Making data-driven intelligence and policy enforcement work together, AWRE revolutionizes cloud optimization in organizations. It creates a new model of autonomous, compliance-aware workload management that takes enterprise cloud operations from reactive provisioning to proactive, intelligent, and automated processes.