A Host-SSD Collaborative Write Accelerator for LSM-Tree-Based Key-Value Stores

Log-Structured Merge (LSM) tree-based Key-Value Stores (KVSs) are widely adopted for their high performance in write-intensive environments, but they often face performance degradation due to write stalls during compaction. Prior solutions, such as regulating I/O traffic or using multiple compaction...

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Bibliographic Details
Published inarXiv.org
Main Authors Kim, KiHwan, Chung, Hyunsun, Ahn, Seonghoon, Park, Junhyeok, Safdar Jamil, Byun, Hongsu, Lee, Myungcheol, Choi, Jinchun, Kim, Youngjae
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.10.2024
Subjects
Buffers
Central processing units
Co-design
CPUs
Hardware
Performance degradation
Performance evaluation
Solid state devices
User interfaces
Workload
Workloads
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Summary:Log-Structured Merge (LSM) tree-based Key-Value Stores (KVSs) are widely adopted for their high performance in write-intensive environments, but they often face performance degradation due to write stalls during compaction. Prior solutions, such as regulating I/O traffic or using multiple compaction threads, can cause unexpected drops in throughput or increase host CPU usage, while hardware-based approaches using FPGA, GPU, and DPU aimed at reducing compaction duration introduce additional hardware costs. In this study, we propose KVACCEL, a novel hardware-software co-design framework that eliminates write stalls by leveraging a dual-interface SSD. KVACCEL allocates logical NAND flash space to support both block and key-value interfaces, using the key-value interface as a temporary write buffer during write stalls. This strategy significantly reduces write stalls, optimizes resource usage, and ensures consistency between the host and device by implementing an in-device LSM-based write buffer with an iterator-based range scan mechanism. Our extensive evaluation shows that for write-intensive workloads, KVACCEL outperforms ADOC by up to 1.17x in terms of throughput and performance-to-CPU-utilization efficiency. For mixed read-write workloads, both demonstrate comparable performance.
ISSN:2331-8422