research-article

Cache-Centric Video Recommendation: An Approach to Improve the Efficiency of YouTube Caches

Authors:

Dilip Kumar Krishnappa,

Carsten Griwodz,

Pål HalvorsenAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 11, Issue 4

Article No.: 48, Pages 1 - 20

https://doi.org/10.1145/2716310

Published: 02 June 2015 Publication History

Abstract

In this article, we take advantage of the user behavior of requesting videos from the top of the related list provided by YouTube to improve the performance of YouTube caches. We recommend that local caches reorder the related lists associated with YouTube videos, presenting the cached content above noncached content. We argue that the likelihood that viewers select content from the top of the related list is higher than selection from the bottom, and pushing contents already in the cache to the top of the related list would increase the likelihood of choosing cached content. To verify that the position on the list really is the selection criterion more dominant than the content itself, we conduct a user study with 40 YouTube-using volunteers who were presented with random related lists in their everyday YouTube use. After confirming our assumption, we analyze the benefits of our approach by an investigation that is based on two traces collected from a university campus. Our analysis shows that the proposed reordering approach for related lists would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. This increase in hit rate would lead to reduction in server load and backend bandwidth usage, which in turn reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTube's content distribution system. An analysis of YouTube's recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.

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Cited By

Lekharu AGupta PSur APatra M(2024)Collaborative Video Caching in the Edge Network using Deep Reinforcement LearningACM Transactions on Internet of Things10.1145/36646135:3(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3664613
Zhou XKe ZQiu T(2024)Recommendation-Driven Multi-Cell Cooperative Caching: A Multi-Agent Reinforcement Learning ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2023.3297213(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3297213
Murakami YFukagawa YKamiyama N(2024)Content Recommendation Considering Cache State2024 IEEE 25th International Conference on High Performance Switching and Routing (HPSR)10.1109/HPSR62440.2024.10635938(233-238)Online publication date: 22-Jul-2024
https://doi.org/10.1109/HPSR62440.2024.10635938
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Index Terms

Cache-Centric Video Recommendation: An Approach to Improve the Efficiency of YouTube Caches
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 11, Issue 4

April 2015

231 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2788342

Editor:
Ralf Steinmetz
Technische Universität Darmstadt, Germany

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Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 June 2015

Accepted: 01 December 2014

Revised: 01 August 2014

Received: 01 March 2014

Published in TOMM Volume 11, Issue 4

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Cited By

Lekharu AGupta PSur APatra M(2024)Collaborative Video Caching in the Edge Network using Deep Reinforcement LearningACM Transactions on Internet of Things10.1145/36646135:3(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3664613
Zhou XKe ZQiu T(2024)Recommendation-Driven Multi-Cell Cooperative Caching: A Multi-Agent Reinforcement Learning ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2023.3297213(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3297213
Murakami YFukagawa YKamiyama N(2024)Content Recommendation Considering Cache State2024 IEEE 25th International Conference on High Performance Switching and Routing (HPSR)10.1109/HPSR62440.2024.10635938(233-238)Online publication date: 22-Jul-2024
https://doi.org/10.1109/HPSR62440.2024.10635938
Tsigkari DIosifidis GSpyropoulos T(2023)Quid pro Quo in Streaming Services: Algorithms for Cooperative RecommendationsIEEE Transactions on Mobile Computing10.1109/TMC.2023.3240006(1-14)Online publication date: 2023
https://doi.org/10.1109/TMC.2023.3240006
Nogueira MPinheiro JAssis FMenasché DSermpezis PSpyropoulos T(2023)When Should Recommenders Account for Low QoS?IEEE Access10.1109/ACCESS.2023.333462311(132014-132036)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3334623
Behera GNain N(2023)The State-of-the-Art and Challenges on Recommendation System’s: Principle, Techniques and Evaluation StrategySN Computer Science10.1007/s42979-023-02207-z4:5Online publication date: 3-Sep-2023
https://doi.org/10.1007/s42979-023-02207-z
Xu HQi HWang YWang PZhang GLiu CJin JZhao XLin ZHu JShao J(2023)PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising ServingMachine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track10.1007/978-3-031-43427-3_40(669-683)Online publication date: 17-Sep-2023
https://doi.org/10.1007/978-3-031-43427-3_40
Giannakas TGiovanidis ASpyropoulos T(2022)MDP-based Network Friendly RecommendationsACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/35131316:4(1-29)Online publication date: 1-Apr-2022
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Rubattu CPalumbo FBhattacharyya SPelcat M(2022)PathTracer: Understanding Response Time of Signal Processing Applications on Heterogeneous MPSoCsACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/35130036:4(1-30)Online publication date: 1-Apr-2022
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Monção ACorrea SViana ACardoso K(2022)Combining Resource-Aware Recommendation and Caching in the Era of MEC for Improving the Experience of Video Streaming UsersIEEE Transactions on Services Computing10.1109/TSC.2022.3205482(1-14)Online publication date: 2022
https://doi.org/10.1109/TSC.2022.3205482
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