- Cloud Computing and Virtualization: It supports multiple storage protocols and can provide block, file, and object storage services for cloud computing platforms. It meets the storage needs of a large number of virtual machines in a virtualized environment, such as offering stable and reliable storage support for enterprise cloud platforms and government cloud platforms.
- Database Applications: With high IOPS and low-latency performance, it can support enterprise mission-critical databases (e.g., Oracle, SQL Server), ensuring efficient reading and writing of databases and data consistency.
- File Sharing: Suitable for internal enterprise file sharing scenarios. Employees can access the shared file system via protocols like NFS and CIFS to realize the sharing and collaboration of documents and data, thereby improving work efficiency.
- High-Performance Computing (HPC): When scientific research institutions, universities, and other entities conduct high-performance computing, it can store and process large volumes of computing data. It provides fast data access support for computing tasks in fields such as weather forecasting, gene sequencing, and material science simulation.
- Big Data Processing: It supports the HDFS interface and can seamlessly integrate with big data platforms like Hadoop. It is used for storing and analyzing massive unstructured data, such as log data, social media data, and Internet of Things (IoT) data.
- Autonomous Driving R&D: In autonomous driving research and development, it can be used to store raw data, training data, and model metadata required by machine learning, high-performance computing, and simulation training clusters, meeting the high requirements for storage performance, reliability, and scalability.
- Broadcasting and Media: It can provide high-performance and massive storage for video editing and program production. It supports multiple editing clients to work in parallel based on a large number of video materials, enabling timely extraction and sharing of resources.
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