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.

Supplementary Material

MP4 File (3488662.3498658.mp4)

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.

Download
599.86 MB

References

[1]

Charu C Aggarwal et al. 2016. Recommender Systems. Springer.

[2]

Parnia Bahar, Tobias Bieschke, and Hermann Ney. 2019. A comparative study on end-to-end speech to text translation. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 792--799.

[3]

Daniel S Berger, Ramesh K Sitaraman, and Mor Harchol-Balter. 2017. Adaptsize: Orchestrating the hot object memory cache in a content delivery network. In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 17). 483--498.

[4]

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics 5 (2017), 135--146.

[5]

Fangfei Chen, Ramesh K Sitaraman, and Marcelo Torres. 2015. End-user mapping: Next generation request routing for content delivery. ACM SIGCOMM Computer Communication Review 45, 4 (2015), 167--181.

Digital Library

[6]

Ludmila Cherkasova. 1998. Improving WWW proxies performance with greedy-dual-size-frequency caching policy. Hewlett-Packard Laboratories.

[7]

Mattia Antonino Di Gangi, Matteo Negri, Roldano Cattoni, Dessi Roberto, and Marco Turchi. 2019. Enhancing transformer for end-to-end speech-to-text translation. In Machine Translation Summit XVII. European Association for Machine Translation, 21--31.

[8]

Fernando Ferraz do Nascimento, Dani Gamerman, and Hedibert Freitas Lopes. 2012. A semiparametric Bayesian approach to extreme value estimation. Statistics and Computing 22, 2 (2012), 661--675.

Digital Library

[9]

Nathan Hartmann, Erick Fonseca, Christopher Shulby, Marcos Treviso, Jessica Rodrigues, and Sandra Aluisio. 2017. Portuguese word embeddings: Evaluating on word analogies and natural language tasks. arXiv preprint arXiv:1708.06025 (2017).

[10]

Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An introduction to Statistical Learning. Vol. 112. Springer.

Digital Library

[11]

Vadim Kirilin, Aditya Sundarrajan, Sergey Gorinsky, and Ramesh K Sitaraman. 2020. RL-Cache: Learning-based cache admission for content delivery. IEEE Journal on Selected Areas in Communications 38, 10 (2020), 2372--2385.

[12]

Samuel Kotz and Saralees Nadarajah. 2000. Extreme value distributions: theory and applications. World Scientific.

[13]

Donghee Lee, Jongmoo Choi, Jong-Hun Kim, Sam H Noh, Sang Lyul Min, Yookun Cho, and Chong Sang Kim. 2001. LRFU: A spectrum of policies that subsumes the least recently used and least frequently used policies. IEEE transactions on Computers 50, 12 (2001), 1352--1361.

[14]

Bruce M Maggs and Ramesh K Sitaraman. 2015. Algorithmic nuggets in content delivery. ACM SIGCOMM Computer Communication Review 45, 3 (2015), 52--66.

Digital Library

[15]

Jose L Martinez-Rodriguez, Aidan Hogan, and Ivan Lopez-Arevalo. 2020. Information extraction meets the semantic web: a survey. Semantic Web 11, 2 (2020), 255--335.

Digital Library

[16]

Iacopo Masi, Yue Wu, Tal Hassner, and Prem Natarajan. 2018. Deep face recognition: A survey. In 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, 471--478.

[17]

Paulo Renato C Mendes, Antonio José G Busson, Sérgio Colcher, Daniel Schwabe, Álan Lívio V Guedes, and Carlos Laufer. 2020. A Cluster-Matching-Based Method for Video Face Recognition. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 97--104.

Digital Library

[18]

Kathlene Morales and Byeong Kil Lee. 2012. Fixed segmented LRU cache replacement scheme with selective caching. In 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC). IEEE, 199--200.

[19]

Diego Moussallem, Ricardo Usbeck, Michael Röeder, and Axel-Cyrille Ngonga Ngomo. 2017. MAG: A multilingual, knowledge-base agnostic and deterministic entity linking approach. In Proceedings of the Knowledge Capture Conference. 1--8.

Digital Library

[20]

R Gary Parker and Ronald L Rardin. 2014. Discrete optimization. Elsevier.

[21]

Rafael Pena, Felipe A Ferreira, Frederico Caroli, Luiz José Schirmer Silva, and Hélio Lopes. 2020. Globo Face Stream: A System for Video Meta-data Generation in an Entertainment Industry Setting. In ICEIS (1). 350--358.

[22]

Mirco Ravanelli, Titouan Parcollet, and Yoshua Bengio. 2019. The pytorch-kaldi speech recognition toolkit. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 6465--6469.

[23]

Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, and Chris Biemann. 2020. Neural entity linking: A survey of models based on deep learning. arXiv preprint arXiv:2006.00575 (2020).

[24]

SM Shahrear Tanzil, William Hoiles, and Vikram Krishnamurthy. 2017. Adaptive scheme for caching YouTube content in a cellular network: Machine learning approach. Ieee Access 5 (2017), 5870--5881.

Cited By

de Sá SPaes Ade A. Rocha A(2024)A Multimodal Approach to Predict Video Popularity from a Large Streaming Service2024 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC61673.2024.10733571(1-7)Online publication date: 26-Jun-2024
https://doi.org/10.1109/ISCC61673.2024.10733571
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

Recommendations

User preference-aware content caching strategy for video delivery in cache-enabled IoT networks
Highlights
- Introduction of a novel learning model founded on VAE, trained with historical user request data, enabling accurate prediction of current users' future preferences.
- Utilization of predicted content preferences to identify popular ...
Abstract
The escalating growth of content-dependent services and applications within the Internet of Things (IoT) platform has led to a surge in traffic, necessitating real-time data processing. Content caching has emerged as an effective solution to ...
CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing
Abstract
Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users’ quality of ...
Cooperative caching for adaptive bit rate streaming in content delivery networks
IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

This work proposes a cooperative caching model which supports adaptive bit-rate streaming in content delivery networks. A linear program (LP) problem is applied to maximize the total user satisfaction. The optimal content placement and content fetching ...

Comments

Information & Contributors

Information

Published In

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]

Sponsors

SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

CoNEXT '21

Sponsor:

SIGCOMM

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

December 7, 2021

Virtual Event, Germany

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
140
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

de Sá SPaes Ade A. Rocha A(2024)A Multimodal Approach to Predict Video Popularity from a Large Streaming Service2024 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC61673.2024.10733571(1-7)Online publication date: 26-Jun-2024
https://doi.org/10.1109/ISCC61673.2024.10733571
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

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten