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Migrating big video data to cloud: a peer-assisted approach for VoD

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Abstract

Since user demand for a Video-on-demand (VoD) service varies with time in one-day period, provisioning self-owned servers for the peak load it must sustain afew hours per day leads to bandwidth under-utilization at other times. Content clouds, e.g. Amazon CloudFront and Azure CDN, let VoD providers pay by bytes for bandwidth resources, potentially leading to cost savings even if the unit rate to rent a machine from a cloud provider is higher than the rate to own one. In addition, recent studies have presented fog computing as a new paradigm to extend the cloud-based platform for a cost-effective and highly scalable service. In this paper, based on long-term traces from two large-scale VoD systems and temporal development model of content clouds, we tackle challenges, design and potential benefits in migrating both Clients/Server-based and peer-assisted VoD services into the hybrid cloud and edge peers in fog computing environment. Our measurements show that the popularity of the most popular videos decays so quickly, for example, by 11% after one hour that it poses large challenges on updating videos in the cloud. However, the trace-driven evaluations show that our proposed migration strategies (active, reactive and smart strategies), although simply based on the current information, can make the hybrid cloud-assisted VoD deployment save up to 30% bandwidth expense compared with the Clients/Server mode. Moreover, they can also handle the flash crowd traffic with little cost. Leveraging the edge peers in fog computing, we propose a cloud-friendly peer replication strategy, which further reduces the migration cost by a factor of 4. Our simulation also shows that the cloud price and server bandwidth chosen play the most important roles in saving cost, while the cloud storage size and cloud content update strategy play the key roles in the user experience improvement.

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Notes

  1. http://www.twitch.tv/

  2. http://www.hulu.com/popular?h=18&page=1&timeframe=today

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Acknowledgments

This research is supported by the following grants: “Multi-source heterogeneous big data management, analysis and mining for urban renewal” (National Natural Science Foundation of China, U1301253), “Application demonstration of big data for land resources management and service” (Science and Technology Planning Project of Guangdong Province, China, 2015B010110006), “Cooperative resource allocation optimization for software-defined network based virtual CDN” (National Natural Science Foundation of China 61602214), Natural Science Foundation of Jiangsu Province in China (BK20160191), National Natural Science Foundation of China (61472212).

Author information

Authors and Affiliations

  1. School of Digital Media, Jiangnan University, Jiangnan, China

    Fei Chen

  2. UCAR Technology (China) Limited, Beijing, China

    Haitao Li

  3. College of Natural Resources and Environment, South China Agricultural University, Guangzhou, China

    Jiangchuan Liu, Bo Li & Yuemin Hu

  4. Department of Computer Science and Technology, Tsinghua University, Haidian Qu, China

    Ke Xu

Authors
  1. Fei Chen
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  2. Haitao Li
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  3. Jiangchuan Liu
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  4. Bo Li
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  5. Ke Xu
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  6. Yuemin Hu
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Corresponding author

Correspondence to Yuemin Hu.

Additional information

This article is part of the Topical Collection: Special Issue on Big Data Networking

Guest Editors: Xiaofei Liao, Song Guo, Deze Zeng, and Kun Wang

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Chen, F., Li, H., Liu, J. et al. Migrating big video data to cloud: a peer-assisted approach for VoD. Peer-to-Peer Netw. Appl. 11, 1060–1074 (2018). https://doi.org/10.1007/s12083-017-0575-3

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  • DOI: https://doi.org/10.1007/s12083-017-0575-3

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