DeltaCFS: Boosting Delta Sync for Cloud Storage Services by Learning from NFS
| Autor: | Yafei Dai, Shenglong Li, Quanlu Zhang, Shouyang Li, Zhenhua Li, Zhi Yang, Yangze Guo |
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| Rok vydání: | 2017 |
| Předmět: |
020203 distributed computing
business.industry Computer science sync 020206 networking & telecommunications Causal consistency Cloud computing 02 engineering and technology computer.software_genre Server Data_FILES 0202 electrical engineering electronic engineering information engineering Operating system Network File System Mobile telephony business File synchronization computer Cloud storage Computer network |
| Zdroj: | ICDCS |
| DOI: | 10.1109/icdcs.2017.77 |
| Popis: | 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 11x less data transfer and up to 100x less computation overhead under concerned workloads. |
| Databáze: | OpenAIRE |
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