Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach
The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (e.g., a short startup delay and smooth playback). One important research interest...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/142290 |
| _version_ | 1811694060798214144 |
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| author | Hu, Wen Jin, Yichao Wen, Yonggang Wang, Zhi Sun, Lifeng |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Hu, Wen Jin, Yichao Wen, Yonggang Wang, Zhi Sun, Lifeng |
| author_sort | Hu, Wen |
| collection | NTU |
| description | The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a large-scale measurement study on users' AP connection and TV series watching patterns using real traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov decision process. The objective is to strike a balance between the increased prefetching and storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first we investigate the performance lower bound by using a random algorithm and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm. |
| first_indexed | 2024-10-01T07:01:34Z |
| format | Journal Article |
| id | ntu-10356/142290 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:01:34Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1422902020-06-18T06:46:19Z Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach Hu, Wen Jin, Yichao Wen, Yonggang Wang, Zhi Sun, Lifeng School of Computer Science and Engineering Engineering::Computer science and engineering Wi-Fi AP Content Prefetching The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a large-scale measurement study on users' AP connection and TV series watching patterns using real traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov decision process. The objective is to strike a balance between the increased prefetching and storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first we investigate the performance lower bound by using a random algorithm and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm. 2020-06-18T06:46:19Z 2020-06-18T06:46:19Z 2017 Journal Article Hu, W., Jin, Y., Wen, Y., Wang, Z., & Sun, L. (2018). Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach. IEEE Transactions on Circuits and Systems for Video Technology, 28(7), 1665-1676. doi:10.1109/TCSVT.2017.2684302 1051-8215 https://hdl.handle.net/10356/142290 10.1109/TCSVT.2017.2684302 2-s2.0-85049405863 7 28 1665 1676 en IEEE Transactions on Circuits and Systems for Video Technology © 2017 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Wi-Fi AP Content Prefetching Hu, Wen Jin, Yichao Wen, Yonggang Wang, Zhi Sun, Lifeng Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach |
| title | Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach |
| title_full | Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach |
| title_fullStr | Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach |
| title_full_unstemmed | Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach |
| title_short | Toward Wi-Fi AP-assisted content prefetching for an on-demand TV series : a learning-based approach |
| title_sort | toward wi fi ap assisted content prefetching for an on demand tv series a learning based approach |
| topic | Engineering::Computer science and engineering Wi-Fi AP Content Prefetching |
| url | https://hdl.handle.net/10356/142290 |
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