[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3465332.3470875acmconferencesArticle/Chapter ViewAbstractPublication PageshotstorageConference Proceedingsconference-collections
research-article
Public Access

A Machine Learning Framework to Improve Storage System Performance

Published: 27 July 2021 Publication History

Abstract

Storage systems and their OS components are designed to accommodate a wide variety of applications and dynamic workloads. Storage components inside the OS contain various heuristic algorithms to provide high performance and adaptability for different workloads. These heuristics may be tunable via parameters, and some system calls allow users to optimize their system performance. These parameters are often predetermined based on experiments with limited applications and hardware. Thus, storage systems often run with these predetermined and possibly suboptimal values. Tuning these parameters manually is impractical: one needs an adaptive, intelligent system to handle dynamic and complex workloads. Machine learning (ML) techniques are capable of recognizing patterns, abstracting them, and making predictions on new data. ML can be a key component to optimize and adapt storage systems. In this position paper, we propose KML, an ML framework for storage systems. We implemented a prototype and demonstrated its capabilities on the well-known problem of tuning optimal readahead values. Our results show that KML has a small memory footprint, introduces negligible overhead, and yet enhances throughput by as much as 2.3x.

Cited By

View all
  • (2024)IDT: Intelligent Data Placement for Multi-tiered Main Memory with Reinforcement LearningProceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658659(69-82)Online publication date: 3-Jun-2024
  • (2024)Hook: A Pattern Locality Guided Prefetch with Enhanced Read Performance for Hybrid SSDs2024 13th Non-Volatile Memory Systems and Applications Symposium (NVMSA)10.1109/NVMSA63038.2024.10693661(1-6)Online publication date: 21-Aug-2024
  • (2024)Enabling Large Dynamic Neural Network Training with Learning-based Memory Management2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00066(788-802)Online publication date: 2-Mar-2024
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
HotStorage '21: Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems
July 2021
119 pages
ISBN:9781450385503
DOI:10.1145/3465332
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

In-Cooperation

  • USENIX Assoc: USENIX Assoc

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. machine learning
  2. operating systems
  3. storage performance optimization
  4. storage systems

Qualifiers

  • Research-article

Funding Sources

Conference

HotStorage '21
Sponsor:

Acceptance Rates

HotStorage '21 Paper Acceptance Rate 15 of 40 submissions, 38%;
Overall Acceptance Rate 34 of 87 submissions, 39%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)697
  • Downloads (Last 6 weeks)76
Reflects downloads up to 21 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)IDT: Intelligent Data Placement for Multi-tiered Main Memory with Reinforcement LearningProceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658659(69-82)Online publication date: 3-Jun-2024
  • (2024)Hook: A Pattern Locality Guided Prefetch with Enhanced Read Performance for Hybrid SSDs2024 13th Non-Volatile Memory Systems and Applications Symposium (NVMSA)10.1109/NVMSA63038.2024.10693661(1-6)Online publication date: 21-Aug-2024
  • (2024)Enabling Large Dynamic Neural Network Training with Learning-based Memory Management2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00066(788-802)Online publication date: 2-Mar-2024
  • (2023)Towards a Machine Learning-Assisted Kernel with LAKEProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575697(846-861)Online publication date: 27-Jan-2023
  • (2023)Improving Storage Systems Using Machine LearningACM Transactions on Storage10.1145/356842919:1(1-30)Online publication date: 19-Jan-2023

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media