research-article

Cache What You Need to Cache: Reducing Write Traffic in Cloud Cache via “One-Time-Access-Exclusion” Policy

Authors:

Ke ZhouAuthors Info & Claims

ACM Transactions on Storage (TOS), Volume 16, Issue 3

Article No.: 18, Pages 1 - 24

https://doi.org/10.1145/3397766

Published: 16 July 2020 Publication History

Abstract

The SSD has been playing a significantly important role in caching systems due to its high performance-to-cost ratio. Since the cache space is typically much smaller than that of the backend storage by one order of magnitude or even more, write density (defined as writes per unit time and space) of the SSD cache is therefore much more intensive than that of HDD storage, which brings about tremendous challenges to the SSD’s lifetime. Meanwhile, under social network workloads, quite a lot writes to the SSD cache are unnecessary. For example, our study on Tencent’s photo caching shows that about 61% of total photos are accessed only once, whereas they are still swapped in and out of the cache. Therefore, if we can predict these kinds of photos proactively and prevent them from entering the cache, we can eliminate unnecessary SSD cache writes and improve cache space utilization.

To cope with the challenge, we put forward a “one-time-access criteria” that is applied to the cache space and further propose a “one-time-access-exclusion” policy. Based on these two techniques, we design a prediction-based classifier to facilitate the policy. Unlike the state-of-the-art history-based predictions, our prediction is non-history oriented, which is challenging to achieve good prediction accuracy. To address this issue, we integrate a decision tree into the classifier, extract social-related information as classifying features, and apply cost-sensitive learning to improve classification precision. Due to these techniques, we attain a prediction accuracy greater than 80%. Experimental results show that the one-time-access-exclusion approach results in outstanding cache performance in most aspects. Take LRU, for instance: applying our approach improves the hit rate by 4.4%, decreases the cache writes by 56.8%, and cuts the average access latency by 5.5%.

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Cited By

Yang JZhang YQiu ZYue YVinayak RDruschel PKaufmann AMace JFlinn JSeltzer M(2023)FIFO queues are all you need for cache evictionProceedings of the 29th Symposium on Operating Systems Principles10.1145/3600006.3613147(130-149)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3600006.3613147
Alwaely BAbhayaratne C(2022)GHOSM: Graph-based Hybrid Outline and Skeleton Modelling for Shape RecognitionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355492219:2s(1-23)Online publication date: 4-Aug-2022
https://dl.acm.org/doi/10.1145/3554922
Zhu HYuan JYang ZZhong XWang ZMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Fine-Grained Fragment Diffusion for Cross Domain Crowd CountingProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548298(5659-5668)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548298
Show More Cited By

Index Terms

Cache What You Need to Cache: Reducing Write Traffic in Cloud Cache via “One-Time-Access-Exclusion” Policy
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing

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Published In

cover image ACM Transactions on Storage

ACM Transactions on Storage Volume 16, Issue 3

August 2020

150 pages

ISSN:1553-3077

EISSN:1553-3093

DOI:10.1145/3410885

Editor:
Sam H. Noh
Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

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Copyright © 2020 ACM.

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Publication History

Published: 16 July 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 February 2020

Received: 01 June 2019

Published in TOS Volume 16, Issue 3

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Cited By

Yang JZhang YQiu ZYue YVinayak RDruschel PKaufmann AMace JFlinn JSeltzer M(2023)FIFO queues are all you need for cache evictionProceedings of the 29th Symposium on Operating Systems Principles10.1145/3600006.3613147(130-149)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3600006.3613147
Alwaely BAbhayaratne C(2022)GHOSM: Graph-based Hybrid Outline and Skeleton Modelling for Shape RecognitionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355492219:2s(1-23)Online publication date: 4-Aug-2022
https://dl.acm.org/doi/10.1145/3554922
Zhu HYuan JYang ZZhong XWang ZMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Fine-Grained Fragment Diffusion for Cross Domain Crowd CountingProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548298(5659-5668)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548298
Li CWu MLiu YZhou KZhang JSun YOshana R(2022)SS-LRUProceedings of the 59th ACM/IEEE Design Automation Conference10.1145/3489517.3530469(397-402)Online publication date: 10-Jul-2022
https://dl.acm.org/doi/10.1145/3489517.3530469
Liu YWang HZhou KLi CWu R(2022)A survey on AI for storageCCF Transactions on High Performance Computing10.1007/s42514-022-00101-34:3(233-264)Online publication date: 23-May-2022
https://doi.org/10.1007/s42514-022-00101-3

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