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Data Management and Storage

How do you optimize cloud-native storage for high-performance computing?

Cloud-native storage is a mechanism for managing persistent data in containerized environments, which is crucial for high-performance computing (HPC). Optimization can improve data throughput and reduce latency, and it is applied in high-IO scenarios such as scientific simulation, AI training, and big data analysis.

The core includes Container Storage Interface (CSI), Persistent Volume (PV/PVC), and StorageClass. The principle is based on distributed file systems (such as Lustre) or high-speed block storage to provide scalability and low latency. This significantly improves the efficiency of HPC tasks, reduces data transmission bottlenecks, and promotes the widespread adoption of cloud-native architecture.

Optimization steps: Evaluate the IO requirements of H applications; select high-performance storage backends such as Alluxio or GPFS; configure Kubernetes StorageClass to define persistent volumes; optimize network paths using RDMA acceleration; implement performance monitoring tools. A typical scenario is gene sequence analysis. Business values include reducing computing costs, accelerating result delivery, and improving resource utilization.

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