Autonomous Cloud Economics: AI-Driven Right-Sizing and Cost Optimization in Hybrid Infrastructures
Shravan Kumar Reddy Padur · International Journal of Scientific Research in Science and Technology · 2018
The explosive adoption of cloud infrastructure has fundamentally reshaped the economics of enterprise computing by shifting investment models from fixed capital expenditure to dynamic operational expenditure. While elasticity and on-demand provisioning have enabled unprecedented scalability, they have also introduced new complexities most notably, the persistent problem of over-provisioning, idle resource consumption, and opaque cost visibility. Traditional cost management approaches relied on static rules and manual thresholding, which failed to capture the temporal variability of workloads. Artificial Intelligence (AI) now provides a paradigm shift through automated right-sizing, continuously analyzing telemetry from compute, storage, and network layers to optimize resource allocation in real time. Leveraging machine learning, anomaly detection, and reinforcement learning algorithms, AI systems can proactively predict demand surges, rebalance workloads, and recommend cost-saving configurations across hybrid and multi-cloud environments. This paper explores the evolution of such intelligent optimization frameworks from heuristic autoscaling to self-learning orchestration—demonstrating how predictive analytics and closed-loop feedback systems ensure an optimal balance between application performance, energy efficiency, and fiscal discipline.