Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity
As video content is responsible for more than 70% of the global IP traffic, related resource allocation approaches, e.g., using content caching, become increasingly important. In this context, to avoid under-provisioning, it is important to rapidly detect and respond to changes in content popularity...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8988163/ |
| _version_ | 1818619757119668224 |
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| author | Sotiris Skaperas Lefteris Mamatas Arsenia Chorti |
| author_facet | Sotiris Skaperas Lefteris Mamatas Arsenia Chorti |
| author_sort | Sotiris Skaperas |
| collection | DOAJ |
| description | As video content is responsible for more than 70% of the global IP traffic, related resource allocation approaches, e.g., using content caching, become increasingly important. In this context, to avoid under-provisioning, it is important to rapidly detect and respond to changes in content popularity dynamics, including volatility, i.e., changes in the second order moment of the underlying process. In this paper, we focus on the early identification of changes in the variance of video content popularity, which we address as a statistical change point (CP) detection problem. Unlike changes in the mean that can be well captured by non-parametric statistical approaches, to address this more demanding problem, we construct a hypothesis test that uses in the test statistic both parametric and non-parametric approaches. In the context of parametric models, we consider linear, in the form of autoregressive moving average (ARMA), and, nonlinear, in the form of generalized autoregressive conditional heteroskedasticity (GARCH) processes. We propose an integrated algorithm that combines off-line and on-line CP schemes, with the off-line scheme used as a training (learning) phase. The algorithm is first assessed over synthetic data; our analysis demonstrates that non parametric and GARCH model based approaches can better generalize and are better suited for content views time series with unknown statistics. Finally, the non-parametric and the GARCH based variations of our proposed integrated algorithm are applied on real YouTube video content views time series, to illustrate the performance of the proposed approach of volatility change detection. |
| first_indexed | 2024-12-16T17:42:33Z |
| format | Article |
| id | doaj.art-e4e8dd424933459284c70d1e7cf291c6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:42:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e4e8dd424933459284c70d1e7cf291c62022-12-21T22:22:33ZengIEEEIEEE Access2169-35362020-01-018304453045710.1109/ACCESS.2020.29726408988163Real-Time Algorithms for the Detection of Changes in the Variance of Video Content PopularitySotiris Skaperas0https://orcid.org/0000-0002-7641-2701Lefteris Mamatas1Arsenia Chorti2Department of Applied Informatics, University of Macedonia, Thessaloniki, GreeceDepartment of Applied Informatics, University of Macedonia, Thessaloniki, GreeceETIS/Université Paris Seine, Université Cergy-Pointoise, ENSEA, CNRS, Cergy, FranceAs video content is responsible for more than 70% of the global IP traffic, related resource allocation approaches, e.g., using content caching, become increasingly important. In this context, to avoid under-provisioning, it is important to rapidly detect and respond to changes in content popularity dynamics, including volatility, i.e., changes in the second order moment of the underlying process. In this paper, we focus on the early identification of changes in the variance of video content popularity, which we address as a statistical change point (CP) detection problem. Unlike changes in the mean that can be well captured by non-parametric statistical approaches, to address this more demanding problem, we construct a hypothesis test that uses in the test statistic both parametric and non-parametric approaches. In the context of parametric models, we consider linear, in the form of autoregressive moving average (ARMA), and, nonlinear, in the form of generalized autoregressive conditional heteroskedasticity (GARCH) processes. We propose an integrated algorithm that combines off-line and on-line CP schemes, with the off-line scheme used as a training (learning) phase. The algorithm is first assessed over synthetic data; our analysis demonstrates that non parametric and GARCH model based approaches can better generalize and are better suited for content views time series with unknown statistics. Finally, the non-parametric and the GARCH based variations of our proposed integrated algorithm are applied on real YouTube video content views time series, to illustrate the performance of the proposed approach of volatility change detection.https://ieeexplore.ieee.org/document/8988163/Content popularity dynamics detectionchange point analysisvariance change detectionvolatility detection |
| spellingShingle | Sotiris Skaperas Lefteris Mamatas Arsenia Chorti Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity IEEE Access Content popularity dynamics detection change point analysis variance change detection volatility detection |
| title | Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity |
| title_full | Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity |
| title_fullStr | Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity |
| title_full_unstemmed | Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity |
| title_short | Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity |
| title_sort | real time algorithms for the detection of changes in the variance of video content popularity |
| topic | Content popularity dynamics detection change point analysis variance change detection volatility detection |
| url | https://ieeexplore.ieee.org/document/8988163/ |
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