AI-Augmented Forecast-Aware Scaling: Leveraging Machine Learning for Predictive Traffic Modeling and Intelligent Cloud Infrastructure Optimization

Abstract

The classical cloud scaling solutions fail to maximize performance while being cost-effective, as digital infrastructures are scaled up and down in response to sporadic workload spikes and demands. In this work, we introduce a new predictive scaling paradigm of the future, AI-Augmented Forecast-Aware Scaling (AFAS), which involves machine learning and intelligent telemetry to forecast traffic variation in advance and automatically optimize infrastructure. The type of model AFAS uses is called a hybrid ensemble learning model. It consists of the combination of gradient boosting, time-series decomposition, and anomaly-conscious regression, which are the principles of high-fidelity workload forecasting. Data from historical and actual times is combined to train the model and validate it to make proactive scaling decisions aligned with business goals. The framework is part of a validation pipeline with a hyperscale mode of deployment that reduces false positive scale events and provides efficient assignments of resources on an AZ basis. Investigations on the cloud-native frameworks identify that 30 percent of overprovisioning is cut, resulting in a better cost-performance ratio, and storage responsiveness to high surges in demand. The findings underscore the revolutionary AI of the perfection of autoscaling techniques in modern cloud systems.