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Container virtualization enables emerging AI workloads such as model serving,highly parallelized training, machine learning pipelines, and so on, to beeasily scaled on demand on the elastic cloud infrastructure. Particularly, AIworkloads require persistent storage to store data such as training inputs,models, and checkpoints. An external storage system like cloud object storageis a common choice because of its elasticity and scalability. To mitigateaccess latency to external storage, caching at a local filesystem is anessential technique. However, building local caches on scaling clusters mustcope with explosive disk usage, redundant networking, and unexpected failures.We propose objcache, an elastic filesystem over external storage. Objcacheintroduces an internal transaction protocol over Raft logging to enable atomicupdates of distributed persistent states with consistent hashing. The proposedtransaction protocol can also manage inode dirtiness by maintaining theconsistency between the local cache and external storage. Objcache supportsscaling down to zero by automatically evicting dirty files to external storage.Our evaluation reports that objcache speeded up model serving startup by 98.9%compared to direct copies via S3 interfaces. Scaling up with dirty filescompleted from 2 to 14 seconds with 1024 dirty files.

Objcache: An Elastic Filesystem over External Persistent Storage for Container Clusters