research-article

SS-LRU: a smart segmented LRU caching

Authors:

Yunqing SunAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 397 - 402

https://doi.org/10.1145/3489517.3530469

Published: 23 August 2022 Publication History

Abstract

Many caching policies use machine learning to predict data reuse, but they ignore the impact of incorrect prediction on cache performance, especially for large-size objects. In this paper, we propose a smart segmented LRU (SS-LRU) replacement policy, which adopts a size-aware classifier designed for cache scenarios and considers the cache cost caused by misprediction. Besides, SS-LRU enhances the migration rules of segmented LRU (SLRU) and implements a smart caching with unequal priorities and segment sizes based on prediction and multiple access patterns. We conducted Extensive experiments under the real-world workloads to demonstrate the superiority of our approach over state-of-the-art caching policies.

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Cited By

Chen RChen XZhao J(2024)Sparsified federated learning with differential privacy for intrusion detection in VANETs based on Fisher Information MatrixPLOS ONE10.1371/journal.pone.030189719:4(e0301897)Online publication date: 17-Apr-2024
https://doi.org/10.1371/journal.pone.0301897
Gu HZhao LHan ZZheng GSong S(2024)AI-Enhanced Cloud-Edge-Terminal Collaborative Network: Survey, Applications, and Future DirectionsIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333815326:2(1322-1385)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3338153
Chen RChen XZhao J(2024)Private and utility enhanced intrusion detection based on attack behavior analysis with local differential privacy on IoVComputer Networks10.1016/j.comnet.2024.110560250(110560)Online publication date: Aug-2024
https://doi.org/10.1016/j.comnet.2024.110560
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Index Terms

SS-LRU: a smart segmented LRU caching
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing

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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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Published: 23 August 2022

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DAC '22

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DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Chen RChen XZhao J(2024)Sparsified federated learning with differential privacy for intrusion detection in VANETs based on Fisher Information MatrixPLOS ONE10.1371/journal.pone.030189719:4(e0301897)Online publication date: 17-Apr-2024
https://doi.org/10.1371/journal.pone.0301897
Gu HZhao LHan ZZheng GSong S(2024)AI-Enhanced Cloud-Edge-Terminal Collaborative Network: Survey, Applications, and Future DirectionsIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333815326:2(1322-1385)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3338153
Chen RChen XZhao J(2024)Private and utility enhanced intrusion detection based on attack behavior analysis with local differential privacy on IoVComputer Networks10.1016/j.comnet.2024.110560250(110560)Online publication date: Aug-2024
https://doi.org/10.1016/j.comnet.2024.110560
Sun PYou LZheng SZhang WMa RYang JWang GZhu FLi SHuang L(2023)Learning-based Data Separation for Write Amplification Reduction in Solid State Drives2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247795(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10247795

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