Abstract

The increasing adoption of globally distributed cloud applications has created significant challenges in maintaining application availability, performance, and resilience across geographic regions. Conventional cloud traffic-management mechanisms primarily rely on predefined routing policies, health probes, latency measurements, and failover rules. Although effective for known conditions, these approaches are generally reactive and may redirect traffic only after service degradation or endpoint failure has already occurred. Artificial intelligence and machine-learning-based predictive analytics provide an opportunity to improve traffic-management decisions by identifying emerging performance degradation before complete service failure.

This paper proposes an AI-Driven Predictive Traffic Management Framework for Microsoft Azure that combines Azure Monitor telemetry, Application Insights, Azure Machine Learning, Azure Front Door, and Azure Traffic Manager to support intelligent workload routing across distributed Azure environments. Historical and real-time telemetry are analyzed to identify patterns associated with increasing latency, regional degradation, workload saturation, and endpoint instability. Machine-learning models estimate the probability of service degradation and provide predictive signals to routing automation. Through architectural analysis and representative use cases, the research examines how predictive traffic management can improve application availability, reduce response latency, and minimize service disruption compared with purely reactive routing approaches.