Monitoring System Practices and Explorations in Heterogeneous AI Platforms

Abstract

Abstract: With the growth of AI's business scale in the vertical field,  the corresponding servers and related services are increasing day by day, there are more and more monitoring objects, and the magnitude of monitoring data is also rising exponentially. Given the vast scale of monitoring data and the diverse consumption needs, the monitoring system requires a modular architecture design for data processing. By collecting and displaying data, the monitoring system can promptly detect the health status, performance indicators, and error conditions of systems or applications, thereby ensuring their stability and reliability. Based on the modular design concept, it supports user-defined rules for monitoring alerts and real-time visualization of monitoring metrics trends, enabling observation of the overall status and instant conditions of various systems and applications. When systems or applications encounter or are about to encounter failures, the monitoring system must respond rapidly with alerts to enable swift resolution or proactive prevention.  Through system architecture design and system performance analysis, this paper demonstrates the notable effects of the monitoring system in enhancing resource utilization, optimizing task execution efficiency, and reducing operational costs with heterogeneous AI computing power is demonstrated.