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Article

Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments

1
Department of Bigdata Analysis & Convergence, Sookmyung Women’s University, Seoul 04310, Korea
2
Graduate School of Data Science, Chonnam National University, Gwangju 61186, Korea
*
Author to whom correspondence should be addressed.
Electronics 2022, 11(8), 1284; https://doi.org/10.3390/electronics11081284
Submission received: 9 March 2022 / Revised: 12 April 2022 / Accepted: 14 April 2022 / Published: 18 April 2022
(This article belongs to the Special Issue Applications of Smart Internet of Things)

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.
Keywords: content popularity; KoBERT; Sentiment Analysis; reinforcement learning; OTT; cloud computing content popularity; KoBERT; Sentiment Analysis; reinforcement learning; OTT; cloud computing

Share and Cite

MDPI and ACS Style

Lee, Y.-S.; Lee, Y.-S.; Jang, H.-R.; Oh, S.-B.; Yoon, Y.-I.; Um, T.-W. Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments. Electronics 2022, 11, 1284. https://doi.org/10.3390/electronics11081284

AMA Style

Lee Y-S, Lee Y-S, Jang H-R, Oh S-B, Yoon Y-I, Um T-W. Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments. Electronics. 2022; 11(8):1284. https://doi.org/10.3390/electronics11081284

Chicago/Turabian Style

Lee, Yeon-Su, Ye-Seul Lee, Hye-Rim Jang, Soo-Been Oh, Yong-Ik Yoon, and Tai-Won Um. 2022. "Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments" Electronics 11, no. 8: 1284. https://doi.org/10.3390/electronics11081284

APA Style

Lee, Y.-S., Lee, Y.-S., Jang, H.-R., Oh, S.-B., Yoon, Y.-I., & Um, T.-W. (2022). Prediction of Content Success and Cloud-Resource Management in Internet-of-Media-Things Environments. Electronics, 11(8), 1284. https://doi.org/10.3390/electronics11081284

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