RAID: Reliability Automation through Intelligent Detection in Large-Scale Ad Systems

Large-scale advertisement systems play a crucial role in the digital economy, yet they often face challenges related to reliability and performance. Addressing these issues is critical for maintaining effective ad serving and ensuring a positive user experience. We present RAID, a framework that utilizes intelligent detection mechanisms to monitor ad performance continuously. By employing advanced machine learning algorithms capable of identifying anomalies, RAID effectively mitigates reliability concerns within ad systems. The hybrid architecture merges real-time data processing with predictive modeling, enabling the anticipation of potential failures. Utilizing reinforcement learning, RAID further refines its detection strategies, optimizing resource allocation while minimizing service disruptions. Results from extensive real-world experiments clearly indicate that RAID diminishes reliability incidents significantly while enhancing overall system performance.