The Design of Trillion-scale SSD-based Indexing with Deterministic Latency for Cloud Block Storage
Cloud block storage (CBS) provides virtual disks with block-level accessibility. The petabyte-scale CBS systems maintain trillions of block-mapping key-value entries as metadata to track the storage location of each virtual block. Although SSD-based KV stores have been widely adopted in cloud systems for their high efficiency and durability, current SSD-based schemes face significant challenges in achieving deterministic access latency for latency-sensitive metadata services. Our experimental observations indicate that the substantial long-tail latency is primarily caused by (1) I/O blocking due to internal tasks of SSDs including modern Zone Namespace SSDs; and (2) additional disk I/Os when querying high-level indexes across memory and SSDs under memory-constrained environments. In this article, we propose an SSD-based SIndex to store trillions of block-mapping entries for latency-critical cloud block storage, which performs comprehensive latency optimization across storage I/O scheduling and high-level indexing. To prevent long-tail I/Os while avoiding intrusive device modifications, SIndex introduces an inter-SSD I/O scheduling mechanism based on read/write separation and SSD state transitions, which mitigates latency fluctuations induced by garbage collection on conventional SSDs and zone operations on Zone Namespace SSDs. Additionally, SIndex employs opportunistic I/O speculation and a concurrent request balancing mechanism to reduce read disturbance and I/O contention. To query the storage location of targeted block-mapping entries with bounded latency, SIndex proposes a memory-efficient high-level index incorporates with a static data layout, preventing time-consuming disk lookups by keeping the index in memory. We evaluate the SIndex prototype using a variety of benchmarks and real-world traces on commodity SSDs. The results demonstrate that SIndex outperforms RocksDB and other approaches by up to 11.4× in tail latency, keeping the 99.99th-percentile latency below 400 μs.
Authors
- Zhandong Guo
- S.L. Wang
- Jun Xu (ORCID: https://orcid.org/0000-0003-1494-8129)
- Kaiye Zhou
- Qiang Cao
Institutions
- China Mobile (China) (CN)
- Wuhan National Laboratory for Optoelectronics (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- ACM Transactions on Storage
- Published
- 2026-01-22
- DOI
- https://doi.org/10.1145/3789205
- Primary Topic
- Advanced Data Storage Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00