Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Yeon-Su | - |
dc.contributor.author | Lee, Ye-Seul | - |
dc.contributor.author | Jang, Hye-Rim | - |
dc.contributor.author | Oh, Soo-Been | - |
dc.contributor.author | Yoon, Yong-Ik | - |
dc.contributor.author | Um, Tai-Won | - |
dc.date.accessioned | 2023-11-08T09:48:36Z | - |
dc.date.available | 2023-11-08T09:48:36Z | - |
dc.date.issued | 2022-04 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/152837 | - |
dc.description.abstract | In Internet-of-Media-Things (IoMT) environments, users can access and view high-quality Over-the-Top (OTT) media services anytime and anywhere. As the number of OTT platform users has increased, the original content offered by such OTT platforms has become very popular, further increasing the number of users. Therefore, effective resource-management technology is an essential aspect for reducing service-operation costs by minimizing unused resources while securing the resources necessary to provide media services in a timely manner when the user's resource-demand rates change rapidly. However, previous studies have investigated efficient cloud-resource allocation without considering the number of users after the release of popular content. This paper proposes a technology for predicting and allocating cloud resources in the form of a Long-Short-Term-Memory (LSTM)-based reinforcement-learning method that provides information for OTT service providers about whether users are willing to watch popular content using the Korean Bidirectional Encoder Representation from Transformer (KoBERT). Results of simulating the proposed technology verified that efficient resource allocation can be achieved by maintaining service quality while reducing cloud-resource waste depending on whether content popularity is disclosed. | - |
dc.format.extent | 17 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/electronics11081284 | - |
dc.identifier.scopusid | 2-s2.0-85128402635 | - |
dc.identifier.wosid | 000785383400001 | - |
dc.identifier.bibliographicCitation | ELECTRONICS, v.11, no.8, pp 1 - 17 | - |
dc.citation.title | ELECTRONICS | - |
dc.citation.volume | 11 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 17 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordAuthor | content popularity | - |
dc.subject.keywordAuthor | KoBERT | - |
dc.subject.keywordAuthor | Sentiment Analysis | - |
dc.subject.keywordAuthor | reinforcement learning | - |
dc.subject.keywordAuthor | OTT | - |
dc.subject.keywordAuthor | cloud computing | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Sookmyung Women's University. Cheongpa-ro 47-gil 100 (Cheongpa-dong 2ga), Yongsan-gu, Seoul, 04310, Korea02-710-9127
Copyright©Sookmyung Women's University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.