KVIMR: Key-Value Store Aware Data Management Middleware for Interlaced Magnetic Recording Based Hard Disk Drive


Yuhong Liang, Tsun-Yu Yang, and Ming-Chang Yang, The Chinese University of Hong Kong


Log-Structured Merge-Tree (LSM-tree) based key-value (KV) store provides write-intensive applications with high throughput on Hard Disk Drive (HDD). Recently, the emerging Interlaced Magnetic Recording (IMR) technology makes the IMR based HDD become another desirable option to construct a cost-effective KV store because of its high areal density. Nevertheless, we observe that deploying LSM-tree based KV store on IMR based HDD may suffer noticeable degradation on throughput of incoming reads/writes. Thus, this paper presents KVIMR, a data management for constructing a cost-effective yet high-throughput LSM-tree based KV store on IMR based HDD. KVIMR is architected as a middleware, interposed between LSM-tree based KV store and IMR based HDD, to embrace the compatibility for mainstream LSM-tree based KV store implementations with limited modifications. Technically, KVIMR adopts a novel Compaction-aware Track Allocation scheme, which leverages the special properties behind the compaction process to remedy the throughput degradation. KVIMR further utilizes a novel Merged RMW approach to improve the efficiency of persisting a multi-track-sized file of KV store into IMR tracks with the ensured crash consistency. Our evaluations on several well-known LSM-tree based KV store implementations reveal that KVIMR not only improves the overall throughput by up to 1.55× under write-intensive workloads but even achieves 2.17× higher throughput under high space usage of HDD, as compared with the state-of-the-art track allocation scheme for IMR.

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@inproceedings {273933,
author = {Yuhong Liang and Tsun-Yu Yang and Ming-Chang Yang},
title = {{KVIMR}: {Key-Value} Store Aware Data Management Middleware for Interlaced Magnetic Recording Based Hard Disk Drive},
booktitle = {2021 USENIX Annual Technical Conference (USENIX ATC 21)},
year = {2021},
isbn = {978-1-939133-23-6},
pages = {657--671},
url = {https://www.usenix.org/conference/atc21/presentation/liang},
publisher = {USENIX Association},
month = jul

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