Zhaoyan Shen

LSM-based key-value stores are widely used due to their write efficiency and scalability. However, their background compaction process requires substantial data movement between the host and storage devices, resulting in inefficient resource usage, frequent write stalls, and degraded overall throughput. Existing optimizations fail to address the performance bottlenecks caused by excessive system interconnect traffic. Although in-storage computing offers a promising way to mitigate data transfer overhead, prior works typically offload all compaction tasks blindly to computational storage devices. Our preliminary experiments show that such naive offloading leads to performance inefficiency due to CSD resource constraints. In this work, we propose CSD-CoKV ⁺ , a host-CSD collaborative framework that enables efficient compaction acceleration for LSM-based KV stores. Specifically, we design a semantic-aware offloader to selectively dispatch suitable compaction jobs to CSDs, together with a lightweight FPGA-based compaction kernel for efficient in-storage execution under constrained device resources. In addition, we develop a range-partitioned data organization with two-level load balancing to preserve locality and improve scalability across CSD arrays, and further introduce a host-device hierarchical cache to better exploit device-level locality in the read path. Evaluations on a real SmartSSD-based prototype show that, with the default host-only baseline, CSD-CoKV ⁺ improves write throughput by up to 2.26 × and boosts compaction throughput by up to 3.42 ×, while improving read throughput by up to 28.5%.

Hybrid SSDs, which allow flash cells to convert among different types of flash cells (e.g., SLC/MLC/QLC), are designed for achieving both high performance and high density. However, previous designs with two types of flash cells encounter a performance cliff degradation once the flash cells of single bit mode (SLC) are consumed. In this work, we propose a novel level-based hybrid SSD (e.g., including SLC-MLC-QLC), named RL-hybridSSD, that adopts an intermediate layer (e.g., MLC) as a performance cushion. We design a workload-aware dynamic placement mechanism that adaptively routes hot and cold writes among SLC, MLC, and QLC regions based on workload dynamics and device space pressure. Further, a multi-agent reinforcement learning-assisted space management scheme is designed to coordinate the garbage collection and mode conversion processes considering both the SSD internal status and workload patterns. We evaluated RL-hybridSSD with various real-world workloads based on simulation. The experimental results show that the proposed RL-hybridSSD provides 2.03× higher performance on average compared with state-of-the-art schemes.

In recent years, Log-Structured Merge Tree (LSM-tree) based key-value stores, such as LevelDB and RocksDB, have been widely adopted in data center systems. Though optimized for high-speed write processing, the severe I/O amplification remains a critical constraint that hinders them from reaching their maximum performance potential. Unfortunately, this problem is deeply rooted in the fundamental design of the LSM-tree structure. A small number of frequently updated key-value items could quickly pollute the entire tree structure, causing repeated changes in the structure and quickly amplifying the amount of disk I/Os across the levels in the tree. In this paper, we present a novel solution, called Log-assisted LSM (L2SM) tree, to effectively mitigate the long-existing I/O amplification problem. L2SM adopts a small-size, multi-level log structure to isolate selected key-value items that have a disruptive effect on the tree structure, accumulates and absorbs the repeated updates in a highly efficient manner, and removes obsolete and deleted key-value items at an early stage. We also establish a systematic foundation, named CAMEL, to analyze the compaction effects of key-value operations. We have prototyped the L2SM structure based on LevelDB. Our experimental evaluation with the YCSB benchmark shows promising results by reducing the amount of disk I/Os by up to 40.2%, increasing the throughput by up to 67.4%, and decreasing the average latency by up to 41%.