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To transcode or not? A machine learning based edge video caching and transcoding strategy

The variable network conditions of end-users demand different resolutions, formats, and bitrate versions of videos to be delivered over the network. Fetching each video from the Content Delivery Network (CDN) burdens all network layers. A promising solution is to leverage Mobile Edge Computing (MEC)...

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Main Authors: Bukhari, S.M.A.H., Baccour, E., Bilal, K., Shuja, J., Erbad, A., Bilal, M.
Format: Article
Published: Elsevier Ltd 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37517/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159463567&doi=10.1016%2fj.compeleceng.2023.108741&partnerID=40&md5=025146309632628bc72c680a316c809b
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spelling oai:scholars.utp.edu.my:375172023-10-04T13:30:42Z http://scholars.utp.edu.my/id/eprint/37517/ To transcode or not? A machine learning based edge video caching and transcoding strategy Bukhari, S.M.A.H. Baccour, E. Bilal, K. Shuja, J. Erbad, A. Bilal, M. The variable network conditions of end-users demand different resolutions, formats, and bitrate versions of videos to be delivered over the network. Fetching each video from the Content Delivery Network (CDN) burdens all network layers. A promising solution is to leverage Mobile Edge Computing (MEC). This paper presents a Machine Learning based caching and transcoding model, which helps release the burden on the backhaul links of the network. The purposed scheme contains a task scheduler and time estimator. The time estimator predicts the job's transcoding time based on the Virtual Machines (VMs) load. The task scheduler maps the transcoding task to different VMs regarding the cost feasibility, Quality of Service (QoS) of the users, and the cost-to-performance ratio of VMs. For this purpose, we prepare a dataset of 500 videos and transcode each video in every lower representation using Amazon Elastic Compute Cloud (EC2). The time estimator is trained on 77 of the video dataset containing more than 80,000 transcoding time data of different videos. The simulation results show that the proposed scheme outperforms its counterparts in terms of cost, average delay perceived by the user, and backhaul burden. © 2023 Elsevier Ltd Elsevier Ltd 2023 Article NonPeerReviewed Bukhari, S.M.A.H. and Baccour, E. and Bilal, K. and Shuja, J. and Erbad, A. and Bilal, M. (2023) To transcode or not? A machine learning based edge video caching and transcoding strategy. Computers and Electrical Engineering, 109. ISSN 00457906 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159463567&doi=10.1016%2fj.compeleceng.2023.108741&partnerID=40&md5=025146309632628bc72c680a316c809b 10.1016/j.compeleceng.2023.108741 10.1016/j.compeleceng.2023.108741 10.1016/j.compeleceng.2023.108741
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The variable network conditions of end-users demand different resolutions, formats, and bitrate versions of videos to be delivered over the network. Fetching each video from the Content Delivery Network (CDN) burdens all network layers. A promising solution is to leverage Mobile Edge Computing (MEC). This paper presents a Machine Learning based caching and transcoding model, which helps release the burden on the backhaul links of the network. The purposed scheme contains a task scheduler and time estimator. The time estimator predicts the job's transcoding time based on the Virtual Machines (VMs) load. The task scheduler maps the transcoding task to different VMs regarding the cost feasibility, Quality of Service (QoS) of the users, and the cost-to-performance ratio of VMs. For this purpose, we prepare a dataset of 500 videos and transcode each video in every lower representation using Amazon Elastic Compute Cloud (EC2). The time estimator is trained on 77 of the video dataset containing more than 80,000 transcoding time data of different videos. The simulation results show that the proposed scheme outperforms its counterparts in terms of cost, average delay perceived by the user, and backhaul burden. © 2023 Elsevier Ltd
format Article
author Bukhari, S.M.A.H.
Baccour, E.
Bilal, K.
Shuja, J.
Erbad, A.
Bilal, M.
spellingShingle Bukhari, S.M.A.H.
Baccour, E.
Bilal, K.
Shuja, J.
Erbad, A.
Bilal, M.
To transcode or not? A machine learning based edge video caching and transcoding strategy
author_facet Bukhari, S.M.A.H.
Baccour, E.
Bilal, K.
Shuja, J.
Erbad, A.
Bilal, M.
author_sort Bukhari, S.M.A.H.
title To transcode or not? A machine learning based edge video caching and transcoding strategy
title_short To transcode or not? A machine learning based edge video caching and transcoding strategy
title_full To transcode or not? A machine learning based edge video caching and transcoding strategy
title_fullStr To transcode or not? A machine learning based edge video caching and transcoding strategy
title_full_unstemmed To transcode or not? A machine learning based edge video caching and transcoding strategy
title_sort to transcode or not? a machine learning based edge video caching and transcoding strategy
publisher Elsevier Ltd
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37517/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159463567&doi=10.1016%2fj.compeleceng.2023.108741&partnerID=40&md5=025146309632628bc72c680a316c809b
_version_ 1779441395836125184
score 13.211869

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