我们实验室在基于混合存储介质优化LSM-tree键值存储的工作被存储领域顶级期刊ACM TOS (CCF推荐A类)接收。向各位参与研究工作的老师、同学、合作者表示祝贺。
论文题目：Leveraging NVMe SSDs for Building A Fast, Cost-EffectiveLSM-tree Based KV Store
论文摘要：Key-Value (KV) stores support many crucial applications and services. They perform fast in-memory process-ing, but are still often limited by I/O performance. The recent emergence of high-speed commodity NVMeSSDs has propelled new KV system designs that take advantage of their ultra-low latency and high bandwidth.Meanwhile, to switch to entirely new data layouts and scale up entire databases to high-end SSDs requiresconsiderable investment.As a compromise, we propose SpanDB, an LSM-tree-based KV store that adapts the popular RocksDB sys-tem to utilizeselective deployment of high-speed SSDs. SpanDB allows users to host the bulk of their data oncheaper and larger SSDs (and even HDDs with certain workloads), while relocating write-ahead logs (WAL)and the top levels of the LSM-tree to a much smaller and faster NVMe SSD. To better utilize this fast disk,SpanDB provides high-speed, parallel WAL writes via SPDK, and enables asynchronous request processingto mitigate inter-thread synchronization overhead and work efficiently with polling-based I/O. To ease thelive data migration between fast and slow disks, we introduce TopFS, a stripped-down file system provid-ing familiar file interface wrappers on top of SPDK I/O. Our evaluation shows that SpanDB simultaneouslyimproves RocksDB’s throughput by up to 8.8×and reduces its latency by 9.5-58.3%. Compared with KVell, a system designed for high-end SSDs, SpanDB achieves 96-140% of its throughput, with a 2.3-21.6×lowerlatency, at a cheaper storage configuration.