DDS: DPU-optimized Disaggregated Storage
Summary: DDS uses DPUs, DMA, zero-copy, userspace I/O, and DPU-resident request execution to optimize disaggregated storage with minimal DBMS changes. It delivers higher throughput, order-of-magnitude lower latency, and saves tens of host CPU cores per server. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Qizhen Zhang (University of Toronto)
- 2. Philip A. Bernstein (Microsoft)
- 3. Badrish Chandramouli (Microsoft)
- 4. Jiasheng Hu (University of Toronto)
- 5. Yiming Zheng (University of Toronto)
BibTeX Citation
@article{zhang_vldb24,
title = {{DDS: DPU-optimized Disaggregated Storage}},
author = {Zhang, Qizhen and Bernstein, Philip A. and Chandramouli, Badrish and Hu, Jiasheng and Zheng, Yiming},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3304--3317},
doi = {10.14778/3681954.3682002},
url = {https://doi.org/10.14778/3681954.3682002},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,464 | DFlush: DPU-Offloaded Flush for Disaggregated LSM-based Key-Value Stores | 2025 | SIGMOD | 5.1727546e-05 |
| 9,687 | DPDPU: Data Processing with DPUs | 2025 | CIDR | 5.1408809e-05 |
| 10,785 | MGI: A Communication Framework for Data Processing in Massive GPU Infrastructures | 2026 | VLDB | 4.9793485e-05 |
| 10,807 | dpKernels: Harvesting DPU Compute Resources for Data-path Efficiency in Cloud Data Processing | 2026 | VLDB | 4.9793485e-05 |
| 11,436 | Analyzing Near-Network Hardware Acceleration with Co-Processing on DPUs | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,772 | Lachesis: Robust Database Storage Management Based on Device-specific Performance Characteristics | 2003 | VLDB |
| 2 | 7,079 | Understanding the Performance Implications of the Design Principles in Storage-Disaggregated Databases | 2024 | SIGMOD |
| 3 | 10,807 | dpKernels: Harvesting DPU Compute Resources for Data-path Efficiency in Cloud Data Processing | 2026 | VLDB |
| 4 | 9,844 | D-RDMA: Bringing Zero-Copy RDMA to Database Systems | 2022 | CIDR |
| 5 | 9,464 | DFlush: DPU-Offloaded Flush for Disaggregated LSM-based Key-Value Stores | 2025 | SIGMOD |
| 6 | 11,436 | Analyzing Near-Network Hardware Acceleration with Co-Processing on DPUs | 2025 | VLDB |
| 7 | 945 | The End of Slow Networks: It's Time for a Redesign | 2016 | VLDB |
| 8 | 4,201 | The Case for Distributed Shared-Memory Databases with RDMA-Enabled Memory Disaggregation | 2023 | VLDB |
| 9 | 3,135 | Rethinking Data Management Systems for Disaggregated Data Centers | 2020 | CIDR |
| 10 | 9,687 | DPDPU: Data Processing with DPUs | 2025 | CIDR |