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Reducing write amplification in flash by death-time prediction of logical block addresses

Authors:

Chandranil Chakraborttii,

Heiner LitzAuthors Info & Claims

SYSTOR '21: Proceedings of the 14th ACM International Conference on Systems and Storage

Article No.: 11, Pages 1 - 12

https://doi.org/10.1145/3456727.3463784

Published: 14 June 2021 Publication History

Abstract

Flash-based solid state drives lack support for in-place updates, and hence deploy a flash translation layer to absorb the writes. For this purpose, SSDs implement a log-structured storage system introducing garbage collection and write-amplification overheads. In this paper, we present a machine learning based approach for reducing write amplification in log structured file systems via death-time prediction of logical block addresses. We define death-time of a data element as the number of I/O writes before which the data element is overwritten. We leverage the sequential nature of I/O accesses to train lightweight, yet powerful, temporal convolutional network (TCN) based models to predict death-times of logical blocks in SSDs. We leverage the predicted death-times in designing ML-DT, a near-optimal data placement technique that minimizes write amplification (WA) in log structured storage systems. We compare our approach with three state-of-the-art data placement schemes and show that ML-DT achieves the lowest WA by utilizing the learnt I/O death-time patterns from real-world storage workloads. Our proposed approach results in up to 14% reduction in write amplification compared to the best baseline technique. Additionally, we present a mapping learning technique to test the applicability of our approach to new or unseen workloads and present a hyper-parameter sensitive study.

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McAllister SWang YBerg BBerger DAmvrosiadis GBeckmann NGanger GGavrilovska ATerry D(2024)FairyWRENProceedings of the 18th USENIX Conference on Operating Systems Design and Implementation10.5555/3691938.3691978(745-764)Online publication date: 10-Jul-2024
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Published In

cover image ACM Conferences

SYSTOR '21: Proceedings of the 14th ACM International Conference on Systems and Storage

June 2021

226 pages

ISBN:9781450383981

DOI:10.1145/3456727

General Chairs:
Bruno Wassermann
IBM Research - Haifa, Israel
,
Michal Malka
IBM Research - Haifa, Israel
,
Program Chairs:
Vijay Chidambaram
University of Texas at Austin/VMWare Research
,
Danny Raz
Technion - Israel Institute of Technology, Israel

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGOPS: ACM Special Interest Group on Operating Systems

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Technion: Israel Institute of Technology
USENIX Assoc: USENIX Assoc

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Association for Computing Machinery

New York, NY, United States

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Published: 14 June 2021

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Samsung Semiconductor Inc.
NSF (National Science Foundation)

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SYSTOR '21

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SIGOPS

SYSTOR '21: The 14th ACM International Systems and Storage Conference

June 14 - 16, 2021

Haifa, Israel

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SYSTOR '21 Paper Acceptance Rate 18 of 63 submissions, 29%;

Overall Acceptance Rate 108 of 323 submissions, 33%

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Cited By

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https://dl.acm.org/doi/10.5555/3691992.3692003
McAllister SWang YBerg BBerger DAmvrosiadis GBeckmann NGanger GGavrilovska ATerry D(2024)FairyWRENProceedings of the 18th USENIX Conference on Operating Systems Design and Implementation10.5555/3691938.3691978(745-764)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691938.3691978
Zhang XBhimani JPei SLee ELee SSeong YKim EChoi CNam EChoi JKim B(2024)Storage Abstractions for SSDs: The Past, Present, and FutureACM Transactions on Storage10.1145/370899221:1(1-44)Online publication date: 30-Dec-2024
https://dl.acm.org/doi/10.1145/3708992
Xie MQian CLitz H(2024)En4S: Enabling SLOs in Serverless Storage SystemsProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698529(160-177)Online publication date: 20-Nov-2024
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Yao XLi QLin KGan XZhang JGao CShen ZXu QYang CXue J(2024)Extremely-Compressed SSDs with I/O Behavior PredictionACM Transactions on Storage10.1145/367704420:4(1-38)Online publication date: 16-Jul-2024
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Sun HSun CTong HYue YQin X(2024)A Machine Learning-Empowered Cache Management Scheme for High-Performance SSDsIEEE Transactions on Computers10.1109/TC.2024.340406473:8(2066-2080)Online publication date: 1-Aug-2024
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Chakraborttii CIslam M(2024)TAP: Transformers Driven Adaptive Prefetching for Hybrid Memory Systems2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI)10.1109/ISCMI63661.2024.10851689(139-146)Online publication date: 22-Nov-2024
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Jaliminche LChakraborttii CChoi CLitz H(2023)Enabling Multi-tenancy on SSDs with Accurate IO Interference ModelingProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624657(216-232)Online publication date: 30-Oct-2023
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Lange TNaor JYadgar G(2023)Offline and Online Algorithms for SSD ManagementCommunications of the ACM10.1145/359620566:7(129-137)Online publication date: 22-Jun-2023
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