High-Performance Scalable Storage for AI Scenarios in Multi-Cloud and Hybrid Cloud Architectures

Why JuiceFS Is Ideal for Hybrid Cloud and Multi-Cloud Environments？

Unified data access across clouds

Compatible with various object storage services.

Supports multiple access protocols such as POSIX, HDFS, S3, and Python SDK, ensuring broad application compatibility.

Cloud-agnostic design

Does not rely on products from a single cloud provider.

Avoids vendor lock-in while enabling cross-cloud functionalities.

Supports cross-cloud disaster recovery, enhancing data availability.

Automated cross-region data distribution

Synchronizes object storage across clouds and regions.

Ensures remote cloud access with performance and throughput equivalent to local access.

Provides an on-demand caching solution to further reduce cross-cloud costs.

Scenario 1: Cross-cloud and cross-region data distribution for large-scale AI applications

Synchronizes metadata and data across regions, allowing remote clusters to achieve local-level latency and IOPS.
Prioritizes access to cached data on local clouds, reducing latency, bandwidth consumption, and costs while improving system performance and stability.
Automatically synchronizes data across multiple regions, significantly reducing the cost of managing multi-cloud data.
Real-time monitoring of metadata synchronization ensures data stability and consistency.

Option 1: Full synchronization of object storage for better performance

Data and metadata are accessed locally, resulting in better data access performance.

Adopter: RunComfy

Option 2: On-demand synchronization of object storage for cost efficiency

Only a distributed cache is used on the remote cloud, and data is preloaded on demand. This avoids bucket replication to significantly reduce storage costs.
You can use idle NVMe SSDs on GPU clusters to store the cache. This further reduces resource costs.

Scenario 2: Same-region, cross-cloud data distribution and disaster recovery

Independent training on multiple clouds with asynchronous bidirectional synchronization of object storage buckets across clouds, ensuring applications are unaware of the transition.
Shared metadata service ensures strong metadata consistency.
Clouds serve as disaster recovery roles for each other. If one cloud's data is inaccessible, you can smoothly switch to another cloud.

Features

Cross-region data replication

Synchronizes historical data via background tasks and asynchronously writes new data from the primary to the backup cloud bucket. In case of a disaster, the backup seamlessly takes over as the primary bucket.

Mirror file systems

Synchronizes metadata unidirectionally across distant clouds, ensuring consistency and timeliness while improving metadata locality and throughput for cross-cloud small file training.