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Cost Optimal Resource Provisioning for Live Video Forwarding Across Video Data Centers

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Big Data Computing and Communications (BigCom 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9784))

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Abstract

Live video forwarding for IP cameras has become a popular service in video data centers. In the forwarding service, requests of end users from different regions arrive in real-time to gain live video streams of IP cameras from inter-connected video data centers. A fundamental scheduling problem is how to assign resources with the global optimal resource cost and forwarding delay to forward live video streams. We introduce the resource provisioning cost as the combination of media server cost, connection bandwidth cost, and forwarding delay cost. In this paper, a multi-objective resource provisioning (MORP) approach is proposed to deal with the online inter-datacenter resource provisioning problem. The approach aims at minimizing the resource provisioning cost during live video forwarding. It adaptively allocates media servers in appropriate video data centers and connects the chosen media servers together to provide system scalability and connectivity. Different from previous works, MORP takes both resource capacity and diversity (e.g. location and price) into consideration during live video forwarding. Finally, the experimental results show that MORP approach not only cuts the resource provisioning cost of 3 % to 10 % comparing to the bench mark approach, but also shortens the resource provisioning delay.

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Acknowledgements

The research reported in this paper is supported by the National Natural Science Foundation of China under Grant No. 61332005 and No. 61190114; The Cosponsored Project of Beijing Committee of Education; The Beijing Training Project for the Leading Talents in S&T (ljrc 201502).

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Correspondence to Yihong Gao .

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Gao, Y., Ma, H., Liu, W., Yu, S. (2016). Cost Optimal Resource Provisioning for Live Video Forwarding Across Video Data Centers. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-42553-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42552-8

  • Online ISBN: 978-3-319-42553-5

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