DeltaCFS: Boosting Delta Sync for Cloud Storage Services by Learning from NFS
Quanlu Zhang, Zhenhua Li, Zhi Yang, Shenglong Li, Yangze Guo, Yafei Dai and Shouyang Li
Peking University, Tsinghua University, Peking University, Peking University, Peking University, Peking University, Peking University

Cloud storage services, such as Dropbox, iCloud Drive, Google Drive, and Microsoft OneDrive, have greatly facilitated users synchronizing files across heterogeneous devices. Among them, Dropbox-like services are particularly beneficial owing to the delta sync functionality that strives towards greater network-level efficiency. However, when delta sync trades computation overhead for network-traffic saving, the tradeoff could be highly unfavorable under some typical workloads. We refer to this problem as the abuse of delta sync. To address this problem, we propose DeltaCFS, a novel file sync framework for cloud storage services by learning from the design of conventional NFS (Network File System). Specifically, we combine delta sync with NFS-like file RPC in an adaptive manner, thus significantly cutting computation overhead on both the client and server sides while preserving the network-level efficiency. DeltaCFS also enables a neat design for guaranteeing causal consistency and fine-grained version control of files. In our FUSE-based prototype system (which is open-source), DeltaCFS outperforms Dropbox by generating up to 11_ less data transfer and up to 100_ less computation overhead under concerned workloads.