ServerlessLSM: Fast RDMA-Codesigned Disaggregated Compaction for Elastic Serverless LSM-Tree Key-Value Store
Mingxuan Liu, Jianhua Gu, Tianhai Zhao · 2025
The Log-Structured Merge-tree (LSM-tree) has become a cornerstone of modern key-value stores (KVSs) due to its efficiency in handling write-intensive workloads. However, traditional monolithic LSM-tree designs suffer from write stalls caused by resource contention between Memtable flushing and SSTable compaction, while existing distributed systems adopt coarse-grained elasticity that limits resource utilization and responsiveness. This paper introduces ServerlessLSM, a kernelspace RDMA-odesigned Serverless workflow architecture for LSM-trees. By decoupling Memtable flushing and compaction into independent Serverless functions, ServerlessLSM enables fine-grained elasticity and low-latency state transfers through distributed OS primitives (e.g., remote fork, remote memory mapping). Evaluations demonstrate that ServerlessLSM reduces cold-start latency by$\mathbf{9 8 \%}$and achieves$\mathbf{2. 4} \times$higher throughput compared to state-of-the-art solutions, while maintaining space amplification below 11 %, validating its efficiency and costeffectiveness in cloud environments.