RaKV: A Write-Optimized LSM Store for Cloud Block Storage with Robust SLA
Yuanhui Zhou, Kai Sheng Lu, Zhonghua Wang, Peng Min Xu, Kai Wang, Ranjun Jia, Jiguang Wan · ACM Transactions on Architecture and Code Optimization · 2025
Building LSM-Tree-based key-value stores (LSM stores) on Cloud Block Storage (CBS) is becoming increasingly popular due to its convenience compared to local block storage devices. However, the constrained bandwidth of CBS and the competition between bandwidth and paid IOPS results in performance degradation and high tail latency in LSM stores, which cannot provide stable service-level agreements (SLAs). We propose RaKV, an efficient LSM store with stable SLAs for CBS, to improve write performance and reduce latency. This store successfully tackles challenges by utilizing instance memory and managing data flow between components. First, RaKV designs a dynamically partitioned L 0 placed in memory and a parallel L 0 - L 1 compaction mechanism to minimize write amplification and write stalls, mitigating the performance impact of the CBS’s constrained bandwidth. Second, RaKV proposes a bandwidth-aware admission control mechanism that dynamically adjusts the number of incoming user requests based on the capacity of the L 0 layer and the available cloud storage bandwidth, thus preventing write stalls and ensuring SLA consistency. Finally, RaKV implements an efficient read/write I/O isolation and scheduling mechanism to coordinate bandwidth and IOPS demands, thus stabilizing read SLAs and reducing read latency. Our evaluation results show that RaKV improves random write performance by 25.8% compared to the state-of-the-art key-value store ADOC, with more consistent throughput and lower read latency than CruiseDB.