AI-Based Capacity Forecasting Models for Elastic Cloud and Hybrid Enterprise Systems

Abstract

Cloud Elasticity and Enterprise Hybrid Systems demand strong capacity planning to achieve performance efficiency, cost-effectiveness and service reliability in the presence of dynamic workloads. Conventional forecasting techniques have a general weakness that is inability to capture the non-stationary and complex behaviour of modern distributed systems. This paper introduces innovative AI-driven capacity prediction models based on deep learning and ensemble machine learning approaches that predict the resource requirements for elastic cloud infrastructures as well as hybrid enterprise-based deployments. These models combine time–series analysis, multi-dimensional system telemetry feature extraction and adaptive learning to model and capture temporal relationships and workload variations. Experimental results based on workload traces from public cloud services and enterprise systems in the wild show that the proposed models consistently outperform classical statistical/machine learning models. Empiric results shows considerably beneficial enhancement on prediction accuracy, lead time and adaptation, cost of over-provisioning without degradation of the quality of service. Results highlight the promise of AI-based forecasting to enable proactive resource management in heterogeneous computing systems, and present a scalable and applicable framework for future autonomous infrastructure operations.