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SBDP: Bandwidth prediction mechanism towards server demands in P2P-VoD system

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Abstract

Migrating popular videos into cloud is an efficient mechanism to decrease the costs of VoD services, but how to get the set of popular videos is a key problem. In this paper, we propose a prediction mechanism named SBDP to evaluate future server bandwidth demands on each video. Firstly, SBDP adopts time-series analysis techniques and history dataset to predict online population in advance. Then in order to obtain initial online population, the curve shape similar (CSS) and Gaussian process regression (GPR) methods are introduced. And finally a multi linear regression (MLR) method with the online population as a main factor is proved and applied in predicting seeds’ upload bandwidth. The proposed methods are simulated on large dataset collected from a popular VoD services in China and the simulation results demonstrate that the prediction data are close to real-world data.

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Acknowledgments

This work is partially supported by National Key Basic Research Program of China (973 Program)(2009CB320504); The Fundamental Research Funds for the Central Universities(2013RC1102); Innovative Research Groups of the National Natural Science Foundation of China (61121061); Important national science & technology specific projects: Next-generation broadband wireless mobile communications network (2010ZX03004-001-01).

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China, 100876

    Xin Cong, Kai Shuang, Sen Su & FangChun Yang

  2. School of Electronic and Information Engineering, Liaoning Technical University, Huludao, China, 125105

    Xin Cong & LingLing Zi

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  1. Xin Cong
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  2. Kai Shuang
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  3. Sen Su
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  4. FangChun Yang
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  5. LingLing Zi
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Correspondence to Kai Shuang.

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Cong, X., Shuang, K., Su, S. et al. SBDP: Bandwidth prediction mechanism towards server demands in P2P-VoD system. Peer-to-Peer Netw. Appl. 8, 501–511 (2015). https://doi.org/10.1007/s12083-014-0273-3

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  • DOI: https://doi.org/10.1007/s12083-014-0273-3

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