The File Storage Challenges for Autonomous Driving teams
- How to manage tens of billions of files in a single namespace?
- How to provide low-latency and high-throughput performance challenges for massive small file training?
- How to keep or lower the TCO even if the rapidly growing data volume brings more storage costs as well as operation and maintenance costs?
- How to make sure that the storage systems are easy to learn, easy to use, easy to maintain, and easy to migrate?
- How to ensure efficient cross-geo and cross-team collaboration?
- How to guarantee data security and compliance towards massive amounts of data?
- How can you get professional services without being locked in by a specific vendor?
- The metadata engine scales out without a single bottleneck and supports tens of billions of files and tens of petabytes of management under one namespace.
- Provide tens of GiB/s read throughput for model training, hundreds of thousands of file reads per second, and millisecond metadata response time.
- Built-in cache management capabilities, pre-training warm-up, p2p cache sharing, automatic cache recovery, etc.
- Distributed cache group to provide low latency, high throughput I/O capabilities for hybrid cloud architectures.
- Relying on the object storage of public clouds as the underlying data storage which ensures that data is safe and secure, the capacity is elastic and scalable, and storage costs are significantly reduced.
- Support data encryption in transit and at rest to address data privacy concerns and compliance needs.
- Fully compatible with POSIX interface, no additional adaptations are required for ML program, transparent access.
- Support applications in all stages of the AI pipeline, unifying data management and improving efficiency.
- Automatic data replication and easier file sharing to accelerate cross-geo team collaboration.
- Fully-managed services to ensure customer business continuity and significantly reduce O&M costs.
- Support both fully managed service and dedicated deployment, making it flexible to meet the multi-cloud, hybrid cloud, or cross-cloud requirements for enterprise cloud strategy.
Solution and Benefits
- JuiceFS can be deployed in public, private, and hybrid clouds to accommodate the flexible and diverse IT resources of enterprises.
- JuiceFS as a unified file store can provide end-to-end storage support from data ingestion to model launching, reducing the O&M costs and the learning cost.
- JuiceFS's built-in cache system provides outstanding performance with low latency and high throughput for autopilot scenarios.
- JuiceFS's compliance and compatibility with multiple protocols provide the most convenient data access. The storage encryption and transmission encryption support ensure flexible and secure data access, sharing and management.
- No vendor-lockin. JuiceFS has a thriving open source community and standard access protocols.