DDS: DPU-optimized Disaggregated Storage
Qizhen Zhang, Philip A. Bernstein, Badrish Chandramouli, Jason Hu, Yiming Zheng
摘要
This paper presents DDS, a novel disaggregated storage architecture enabled by emerging networking hardware, namely DPUs (Data Processing Units). DPUs can optimize the latency and CPU consumption of disaggregated storage servers. However, utilizing DPUs for DBMSs requires careful design of the network and storage paths and the interface exposed to the DBMS. To fully benefit from DPUs, DDS heavily uses DMA, zero-copy, and userspace I/O to minimize overhead when improving throughput. It also introduces an offload engine that eliminates host CPUs by executing client requests directly on the DPU. Adopting DDS' API requires minimal DBMS modification. Our experimental study and production system integration show promising results---DDS achieves higher disaggregated storage throughput with an order of magnitude lower latency, and saves up to tens of CPU cores per storage server.
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引用它的顶会 Paper8
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- DShuffle: DPU-Optimized Shuffle Framework for Large-scale Data ProcessingChen Ding, Sicen Li, Kai Lu, Ting Yao 等USENIX ATC 2025 · 被引用 2 次
- Efficient and Flexible Datapaths for Fine-Grained Rack-Scale Interconnects with Elastic QPChenxingyu Zhao, Yibo Wu, Hongtao Zhang, Jaehong Min 等SIGCOMM 2026 · 被引用 1 次
- PD3: Prefetching Data with DPUs for Disaggregated MemorySidharth Sankhe, Felix Zhang, Umayrah Chonee, Sherman Lim 等NSDI 2026 · 被引用 1 次
- MGI: A Communication Framework for Data Processing in Massive GPU InfrastructuresDi Wu, Hongshi Tan, Hanzhang Yang, Bingsheng He 等VLDB 2026
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