›› 2017, Vol. 32 ›› Issue (3): 555-570.doi: 10.1007/s11390-017-1743-x

Special Issue: Computer Graphics and Multimedia

• Computer Network and Information Security • Previous Articles     Next Articles

Minimizing Resource Cost for Camera Stream Scheduling in Video Data Center

Yi-Hong Gao, Hua-Dong Ma, Fellow, CCF, Wu Liu, Member, CCF   

  1. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2016-05-20 Revised:2016-12-03 Online:2017-05-05 Published:2017-05-05
  • Contact: 10.1007/s11390-017-1743-x
  • About author:Yi-Hong Gao is now a Ph.D. candidate of Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia and the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing. His current research mainly focuses on resource scheduling approach, video data center, cloud computing.
  • Supported by:

    The research is supported by the National Natural Science Foundation of China-Guangdong Joint Fund under Grant No. U1501254, the National Natural Science Foundation of China under Grant No. 61332005, the Funds for Creative Research Groups of China under Grant No. 61421061, the Beijing Training Project for the Leading Talents in Science and Technology under Grant No. ljrc 201502, and the Cosponsored Project of Beijing Committee of Education.

Video surveillance service, which receives live streams from IP cameras and forwards the streams to end users, has become one of the most popular services of video data center. The video data center focuses on minimizing the resource cost during resource provisioning for the service. However, little of the previous work comprehensively considers the bandwidth cost optimization of both upload and forwarding streams, and the capacity of the media server. In this paper, we propose an efficient resource scheduling approach for online multi-camera video forwarding, which tries to optimize the resource sharing of media servers and the networks together. Firstly, we not only provide a fine-grained resource usage model for media servers, but also evaluate the bandwidth cost of both upload and forwarding streams. Without loss of generality, we utilize two resource pricing models with different resource cost functions to evaluate the resource cost: the linear cost function and the non-linear cost functions. Then, we formulate the cost minimization problem as a constrained integer programming problem. For the linear resource cost function, the drift-plus-penalty optimization method is exploited in our approach. For non-linear resource cost functions, the approach employs a heuristic method to reduce both media server cost and bandwidth cost. The experimental results demonstrate that our approach obviously reduces the total resource costs on both media servers and networks simultaneously.

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