JMStore: Joint Optimization of Computation and Storage Balancing in Multi-NDP Key-Value Stores with Hash-Based Data Distribution
Hui Sun, Jiaming Huang, Bo Chen, Yinliang Yue, Xiao Qin · ACM Transactions on Storage · 2025
It is challenging to store and process massive unstructured data for key-value storage systems that require high concurrency, high performance, and low latency. Log-Structured Merge (LSM) trees-based Key-value stores or KV stores are widely adopted for enhanced write performance. Existing KV stores are primarily deployed on a CPU-centric architecture, which necessitates moving data to the CPU from memory or storage devices for processing. This is particularly problematic during the compaction process, which involves substantial data movement and rewrite. This tradition consumes bandwidth and computational resources, leading to write amplification and impairing system performance. Near-Data Processing (NDP) devices mitigate this issue by processing data at the storage location, thereby reducing data movement cost. Recognizing that the computational power of a single NDP device is insufficient for the demands of large-scale unstructured data processing, we propose JMStore—a multi-NDP key-value store based on a hash data organization. By offloading computational tasks to multiple NDP devices, JMStore collaboratively optimizes the compaction process to address the data movement issue as well as the mismatch between large-scale in workloads data and computational power on an NDP. We design a key-value store programming model and data organization for a multi-NDP architecture, enabling the system to leverage the hardware efficiency and parallelism of multiple NDP devices to significantly optimize system performance. We also propose a strategy to balance storage and computation resources under a hash layout. Compared to the latest single NDP KV store (PStore) and the multi-NDP KV store (MStore), JMStore demonstrates significant performance improvements under DB_Bench and YCSB-C with read-write mixed workloads: the peak improvements can reach a factor of 10.