Building an Elastic Block Storage over EBOFs Using Shadow Views
Sheng Jiang, Ming Liu
Abstract
The EBOF (Ethernet-Bunch-Of-Flash) has emerged as an enticing and promising disaggregated storage platform due to its streamlined I/O processing, high scalability, and substantial energy/cost-efficiency improvement. An EBOF applies a smart-sender dumb-receiver design philosophy and provides backward-compatible storage volumes to expedite system deployment. Yet, the static and opaque internal I/O processing pipeline lacks resource allocation, I/O scheduling, and traffic orchestration capabilities, entailing bandwidth waste, workload non-adaptiveness, and performance interference.
This paper presents the design and implementation of a distributed telemetry system (called shadow view) to tackle the above challenges and facilitate the effective use of an EBOF. We model an EBOF as a two-layer multi-switch architecture and develop a view development protocol to construct the EBOF running snapshot and expose internal execution statistics at runtime. Our design is motivated by the observation that fast data center networks make the overheads of interserver communication and synchronization negligible. We demonstrate the effectiveness of shadow view by building a block storage (dubbed Flint 1 ) atop EBOFs. The enhanced I/O data plane allows us to develop three new techniques-an elastic volume manager, an eIO scheduler, and a view-enabled bandwidth auction mechanism. Our evaluations using the Fungible FS1600 EBOF show that a Flint volume achieves 9.3/9.2 GB/s read/write bandwidth with no latency degradation, significantly outperforming the defacto EBOF volume. It achieves up to 2.9× throughput improvements when running an object store. Flint is tenant-aware and remote target-aware, delivering efficient multi-tenancy and workload adaptiveness.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 7df9ae15-ba41-42b3-b77d-91a7eb9837a7Cited by top-tier papers5
- Understanding and Profiling CXL.mem Using PathFinderXiao Li, Zerui Guo, Yuebin Bai, Mahesh Ketkar et al.SIGCOMM 2025 · 6 citations
- Co-Designing Traffic Control with NVMe-oF for Disaggregated Storage: A Comparative Study of Switched and Switchless SAN ArchitecturesChendong Wang, Joontaek Oh, Ming LiuNSDI 2026 · 3 citations
- Building A CSFQ-Inspired Transport for Switched CXL Memory PoolingZerui Guo, Emily Shriver, Ming LiuNSDI 2026 · 2 citations
- Understanding and Optimizing Database Pushdown on Disaggregated StorageHua Zhang, Xiao Li, Yuebin Bai, Ming LiuASPLOS 2026 · 1 citation
- Espresso: Constructing Cost-Efficient CXL JBOF via Inter-SSD Computing Resource SharingShushu Yi, Yuda An, Li Peng, Xiurui Pan et al.OSDI 2026
Builds on18
- Building An Elastic Query Engine on Disaggregated StorageMidhul Vuppalapati, Justin Miron, Rachit Agarwal, Dan Truong et al.NSDI 2020 · 142 citations
- Programmable Calendar Queues for High-speed Packet SchedulingNaveen Kr. Sharma, Chenxingyu Zhao, Ming Liu, Pravein G. Kannan et al.NSDI 2020 · 119 citations
- Empowering Azure Storage with RDMAWei Bai, Shanim Sainul Abdeen, Ankit Agrawal, Krishan Kumar Attre et al.NSDI 2023 · 117 citations
- From luna to solar: the evolutions of the compute-to-storage networks in Alibaba cloudRui Miao, Lingjun Zhu, Shu Ma, Kun Qian et al.SIGCOMM 2022 · 79 citations
- Assise: Performance and Availability via Client-local NVM in a Distributed File SystemThomas E. Anderson, Marco Canini, Jongyul Kim, Dejan Kostic et al.OSDI 2020 · 71 citations
Related papers
- Disaggregated RAID Storage in Modern DatacentersJunyi Shu, Ruidong Zhu, Yun Ma, Gang Huang et al.ASPLOS 2023 · 18 citations
- LEED: A Low-Power, Fast Persistent Key-Value Store on SmartNIC JBOFsZerui Guo, Hua Zhang, Chenxingyu Zhao, Yuebin Bai et al.SIGCOMM 2023 · 19 citations
- Scalio: Scaling up DPU-based JBOF Key-value Store with NVMe-oF Target OffloadXun Sun, Mingxing Zhang, Yingdi Shan, Kang Chen et al.OSDI 2025 · 2 citations
- Unleashing The Potential of Datacenter SSDs by Taming Performance VariabilityGohar Irfan Chaudhry, Ankit Bhardwaj, Zhenyuan Ruan, Adam BelayNSDI 2026
- NVMe-oAF: Towards Adaptive NVMe-oF for IO-Intensive Workloads on HPC CloudArjun Kashyap, Xiaoyi LuHPDC 2022 · 10 citations
