How do you monitor the performance of cloud-native storage systems?

Monitoring the performance of cloud-native storage systems involves continuously observing and analyzing their metrics to ensure data reliability, low latency, and high availability. This is crucial because performance issues can lead to system bottlenecks or data loss, especially in widely used scenarios such as microservices, containerization, and high concurrency, like Kubernetes clusters.

Its core components include metric collection tools (e.g., Prometheus), visualization platforms (e.g., Grafana), and key metrics such as IOPS, latency, error rate, and throughput. It is characterized by automation, elastic scalability, and real-time capabilities. In practical applications, storage configurations are optimized through CNCF ecosystem tools, which affect system efficiency and data persistence, supporting the stable operation of complex workloads.

Implementation steps are: integrating cloud-native monitoring tools; defining performance baselines; setting up real-time alerts; analyzing logs and optimizing configurations. Business values include improving reliability, reducing operational costs, and enhancing user experience, with typical scenarios including efficient support for AI training or serverless applications.