research-article

Feedforward Neural Networks for Caching: n Enough or Too Much?

Authors:

Vladyslav Fedchenko,

Giovanni Neglia,

Bruno RibeiroAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 46, Issue 3

Pages 139 - 142

https://doi.org/10.1145/3308897.3308958

Published: 25 January 2019 Publication History

Abstract

We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications.

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Cited By

Bellavita JSim CWu KSim AYoo SIto HGaronne VLancon E(2024)Understanding Data Access Patterns for dCache SystemEPJ Web of Conferences10.1051/epjconf/202429501053295(01053)Online publication date: 6-May-2024
https://doi.org/10.1051/epjconf/202429501053
Lin TChen YYang HZhang YJiang BYan J(2024)DeCa360: Deadline-aware edge caching for two-tier 360° video streamingJournal of Network and Computer Applications10.1016/j.jnca.2024.104022232(104022)Online publication date: Dec-2024
https://doi.org/10.1016/j.jnca.2024.104022
Faticanti FNeglia G(2024)Optimistic online caching for batched requestsComputer Networks10.1016/j.comnet.2024.110341244(110341)Online publication date: May-2024
https://doi.org/10.1016/j.comnet.2024.110341
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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 46, Issue 3

December 2018

174 pages

ISSN:0163-5999

DOI:10.1145/3308897

Editor:
Nidhi Hegde
Borealis AI

Issue’s Table of Contents

Copyright © 2019 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 January 2019

Published in SIGMETRICS Volume 46, Issue 3

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Cited By

Bellavita JSim CWu KSim AYoo SIto HGaronne VLancon E(2024)Understanding Data Access Patterns for dCache SystemEPJ Web of Conferences10.1051/epjconf/202429501053295(01053)Online publication date: 6-May-2024
https://doi.org/10.1051/epjconf/202429501053
Lin TChen YYang HZhang YJiang BYan J(2024)DeCa360: Deadline-aware edge caching for two-tier 360° video streamingJournal of Network and Computer Applications10.1016/j.jnca.2024.104022232(104022)Online publication date: Dec-2024
https://doi.org/10.1016/j.jnca.2024.104022
Faticanti FNeglia G(2024)Optimistic online caching for batched requestsComputer Networks10.1016/j.comnet.2024.110341244(110341)Online publication date: May-2024
https://doi.org/10.1016/j.comnet.2024.110341
Ou PChen NHuang Z(2024)Recommendation-Aware Collaborative Edge Caching Strategy in the Internet of VehiclesWireless Artificial Intelligent Computing Systems and Applications10.1007/978-3-031-71470-2_6(65-77)Online publication date: 13-Nov-2024
https://doi.org/10.1007/978-3-031-71470-2_6
Yang JMao ZYue YRashmi KNaor DGoel A(2023)GL-CacheProceedings of the 21st USENIX Conference on File and Storage Technologies10.5555/3585938.3585946(115-133)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.5555/3585938.3585946
Qiu SFan QLi XZhang XMin GLyu Y(2023)OA-Cache: Oracle Approximation-Based Cache Replacement at the Network EdgeIEEE Transactions on Network and Service Management10.1109/TNSM.2023.323966420:3(3177-3189)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TNSM.2023.3239664
Cui LNi EZhou YWang ZZhang LLiu JXu Y(2023)Towards Real-Time Video Caching at Edge Servers: A Cost-Aware Deep Q-Learning SolutionIEEE Transactions on Multimedia10.1109/TMM.2021.312580325(302-314)Online publication date: 2023
https://doi.org/10.1109/TMM.2021.3125803
Maher SEbrahim GHosny SSalah M(2023)A Cache-Enabled Device-to-Device Approach Based on Deep LearningIEEE Access10.1109/ACCESS.2023.329728011(76953-76963)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3297280
Hasslinger GOkhovatzadeh MNtougias KHasslinger FHohlfeld O(2023)An overview of analysis methods and evaluation results for caching strategiesComputer Networks10.1016/j.comnet.2023.109583228(109583)Online publication date: Jun-2023
https://doi.org/10.1016/j.comnet.2023.109583
Eris FLouis MEris KAbellán JJoshi A(2022)Puppeteer: A Random Forest Based Manager for Hardware Prefetchers Across the Memory HierarchyACM Transactions on Architecture and Code Optimization10.1145/357030420:1(1-25)Online publication date: 16-Dec-2022
https://dl.acm.org/doi/10.1145/3570304
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