research-article

An ML-Based Approach for Near Real-Time Content Caching

Authors:

Bruno Guimarães Oliveira,

Paulo Renato C. Mendes,

Yago CoelhoAuthors Info & Claims

VisNEXT'21: Proceedings of the Workshop on Design, Deployment, and Evaluation of Network-assisted Video Streaming

Pages 8 - 14

https://doi.org/10.1145/3488662.3498658

Published: 07 December 2021 Publication History

Abstract

Content caching is a well-known promising solution to address large demands for streaming companies. This paper presents an ongoing work towards improving CDN network traffic focusing on users' quality of experience (QoE) by anticipating which videos will be popular on Globo's platform. To do so, a deep neural network approach was chosen to model video's popularity based on its metadata and a near real-time framework is presented describing how to make content caching in a preemptive way. Additionally, a threshold selection approach is presented defining whether a video should be cached or not. The presented approach allows making content cache without any user interaction, aiming to decide about the admission of the content before it starts to receive requests. This approach is important to most of the daily published videos at Globo, especially for breaking news. Using Globo's real-world data, we demonstrate the popularity predictor results and conclude with some directions for future works.

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Content caching is a well-known promising solution to address large demands for streaming companies. This paper presents an ongoing work towards improving CDN network traffic focusing on users' quality of experience (QoE) by anticipating which videos will be popular on Globo's platform. To do so, a deep neural network approach was chosen to model video's popularity based on its metadata and a near real-time framework is presented describing how to make content caching in a preemptive way. Additionally, a threshold selection approach is presented defining whether a video should be cached or not. The presented approach allows making content cache without any user interaction, aiming to decide about the admission of the content before it starts to receive requests. This approach is important to most of the daily published videos at Globo, especially for breaking news. Using Globo's real-world data, we demonstrate the popularity predictor results and conclude with some directions for future works.

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Cited By

Mendes PRodrigues LSoares JSerra ACoelho YTuratti NRocha AMenasché D(2022)Aumentando a Eficiência do Cache Proativo com Algoritmos de Mochilas para PoPs e Hashes para ServidoresAnais do XXIII Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD 2022)10.5753/wscad.2022.226307(253-264)Online publication date: 19-Oct-2022
https://doi.org/10.5753/wscad.2022.226307

Index Terms

An ML-Based Approach for Near Real-Time Content Caching
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Network resources allocation

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cover image ACM Conferences

VisNEXT'21: Proceedings of the Workshop on Design, Deployment, and Evaluation of Network-assisted Video Streaming

December 2021

31 pages

ISBN:9781450391375

DOI:10.1145/3488662

Copyright © 2021 ACM.

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]

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

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Published: 07 December 2021

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CoNEXT '21

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SIGCOMM

CoNEXT '21: The 17th International Conference on emerging Networking EXperiments and Technologies

December 7, 2021

Virtual Event, Germany

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Cited By

Mendes PRodrigues LSoares JSerra ACoelho YTuratti NRocha AMenasché D(2022)Aumentando a Eficiência do Cache Proativo com Algoritmos de Mochilas para PoPs e Hashes para ServidoresAnais do XXIII Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD 2022)10.5753/wscad.2022.226307(253-264)Online publication date: 19-Oct-2022
https://doi.org/10.5753/wscad.2022.226307

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