AIOPS for Predictive Infrastructure Scaling

Abstract

Cloud-based applications rely on auto- scaling mechanisms to maintain performance and availability. However, traditional reactive scaling methods often lead to inefficient resource utilization, increased costs, and potential downtime. To address these challenges, this project proposes an AI-driven predictive infrastructure scaling system that utilizes Kubernetes, Prometheus, Python (ML), and hpa auto scaling.

This system leverages machine learning models to analyze historical and real-time infrastructure metrics to forecast demand spikes and failures before they occur. By proactively adjusting cloud resources, itensures optimized performance, minimal latency, and cost efficiency. The predictive model is built using time-series forecasting and anomaly detection techniques, enabling preemptive scaling of resources rather than reactive responses.

By implementing AIOps (Artificial Intelligence for IT Operations), this approach significantly reduces downtime, over-provisioning, and manual intervention, making cloud infrastructure management more autonomous and intelligent. The result is a highly resilient, self-scaling system capable of maintaining service continuity under dynamic workload.