How do you optimize cloud-native storage for low-latency applications?

Low-latency applications require millisecond-level data access, and cloud-native storage provides persistent data services in containerized environments such as Kubernetes. Optimizing storage is crucial for scenarios like real-time processing, financial transactions, and edge computing, as it enhances application responsiveness and reliability.

Core optimizations include using Local Persistent Volumes (Local PV) with high-performance NVMe SSD devices, enabling direct hardware access through the Container Storage Interface to reduce network latency and I/O paths. Additionally, custom storage classes and I/O scheduling policies can improve throughput. In practical applications, Kubernetes' StorageClass supports rapid deployment of high-performance storage, significantly improving data-intensive tasks such as AI inference.

Implementation steps: 1. Deploy the local PV driver and configure a low-latency StorageClass. 2. Allocate PVCs using high-performance backend storage for applications. 3. Optimize the file system and monitor I/O metrics. A typical scenario, such as edge video stream processing, can reduce latency to the microsecond level, leading to higher user satisfaction and business efficiency.