Minimizing Resource Cost for Camera Stream Scheduling in Video Data Center

Video surveillance service has become one of the most popular services of video data center. Different from traditional VoD and IPTV services, video surveillance service is based on video data center which receives video stream from IP camera and forward the video stream to end user. However, current works only consider the forwarding video stream scheduling from media server to end user and focus on minimizing media server usage during video delivery. It is lack of a full consideration on network cost optimization of both receiving and forwarding streams as well as capacity evaluation of the media server. In this paper, we present a video surveillance service based on data center and propose an efficient resource scheduling approach for online multi-camera video delivery. We not only provide a fine-grained resource usage model for media servers, but also optimize network resources on both receiving and forwarding streams in video data center. We formulate the resource scheduling problem as a constrained integer optimization problem to minimize the total resource cost. We first propose an optimal solution for linear resource cost function using drift-plus-penalty optimization method. For non-linear resource cost functions, we present a heuristic scheduling approach to reduce both media server cost and network cost. The experimental results show that our approaches obviously decrease the total resource cost of the video data center on both media servers and networks.