A Predictive Framework for Early Warning and Risk-Aware Forecasting of Azure Subnet IP Exhaustion

The efficient management of IP address space within virtual networks plays a critical role in ensuring uninterrupted operations, especially as more organizations adopt cloud-based infrastructures. In this study, an AI-based framework is presented to predict potential exhaustion of Azure subnet IPs by analyzing historical time-series data. Addressing issues like interruptions in automation and delays in resource allocation, the proposed approach implements automated data gathering via Azure Automation paired with Log Analytics, enabling the creation of daily records of subnet utilization. The ARIMA (Autoregressive Integrated Moving Average) model is selected due to its proven capability in identifying seasonal variations, tracking long-term usage patterns, and producing reliable forecasts for a range of subnet consumption scenarios. Enhancing readiness, the system provides advance warning signals for anticipated periods of high IP utilization, together with a risk assessment feature that classifies subnets according to their likelihood of depletion. This methodology encourages proactive resource management and facilitates automated notifications and recovery mechanisms within Azure, offering a scalable addition to existing IP address management frameworks.