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research-article

Queue-based and learning-based dynamic resources allocation for virtual streaming media server cluster of multi-version VoD system

Authors:

Feng LiuAuthors Info & Claims

Volume 78, Issue 15

Pages 21827 - 21852

https://doi.org/10.1007/s11042-019-7457-z

Published: 01 August 2019 Publication History

Abstract

Nowadays, video-on-demand (VoD) providers offer multiple-quality video streaming services to users, called as multi-version VoD. Unlike traditional VoD, multi-version VoD providers should consider to allocate bandwidth resource and transcoding computation resource simultaneously. However, most of existing resource allocation works only focused on cost reduction or bandwidth optimization, and they did not consider to allocate transcoding computation resources for multi-version VoD systems. Therefore, how to allocate bandwidth resource and transcoding computation resource simultaneously for multi-version VoD systems is still one major challenge. In this paper, we propose a queue-based and learning-based dynamic resources allocation strategy (QLRA) for virtual streaming media server cluster of multi-version VoD system. First, we analyze the user behavior habits and build the virtual streaming media server cluster as an M/G/n queue system. Based on queueing theory, we can allocate initial resources for virtual streaming media server cluster of multi-version VoD system. Second, taking the changes of the user arrival rate and the workload of multi-version VoD system as feedbacks, we introduce learning automaton to allocate resources dynamically for virtual streaming media server cluster. Third, we evaluate QLRA with other methods, and results show the correctness and effectiveness of our strategy.

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Cited By

Chang ZChan S(2023)Bi-Criteria Approximation for a Multi-Origin Multi-Channel Auto-Scaling Live Streaming CloudIEEE Transactions on Multimedia10.1109/TMM.2022.315209325(2839-2850)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3152093
Belgacem A(2022)Dynamic resource allocation in cloud computing: analysis and taxonomiesComputing10.1007/s00607-021-01045-2104:3(681-710)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s00607-021-01045-2
Chang ZChan S(2021)An Approximation Algorithm to Maximize User Capacity for an Auto-Scaling VoD SystemIEEE Transactions on Multimedia10.1109/TMM.2020.303106823(3714-3725)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TMM.2020.3031068

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 78, Issue 15

Aug 2019

1603 pages

ISSN:1380-7501

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Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2019

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Cited By

Chang ZChan S(2023)Bi-Criteria Approximation for a Multi-Origin Multi-Channel Auto-Scaling Live Streaming CloudIEEE Transactions on Multimedia10.1109/TMM.2022.315209325(2839-2850)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3152093
Belgacem A(2022)Dynamic resource allocation in cloud computing: analysis and taxonomiesComputing10.1007/s00607-021-01045-2104:3(681-710)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s00607-021-01045-2
Chang ZChan S(2021)An Approximation Algorithm to Maximize User Capacity for an Auto-Scaling VoD SystemIEEE Transactions on Multimedia10.1109/TMM.2020.303106823(3714-3725)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TMM.2020.3031068

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