Video Data Center Research Articles

StreamTune: dynamic resource scheduling approach for workload skew in video data center

Video surveillance applications need video data center to provide elastic virtual machine (VM) provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The unknown arrival workloads easily lead to workload skew among VMs. In this paper, we study how to balance the workload skew on online video surveillance system. First, we design the system framework for online surveillance service which consists of video capturing and analysis tasks. Second, we propose StreamTune, an online resource scheduling approach for workload balancing, to deal with irregular video analysis workload with the minimum number of VMs. We aim at timely balancing the workload skew on video analyzers without depending on any workload prediction method. Furthermore, we evaluate the performance of the proposed approach using a traffic surveillance application. The experimental results show that our approach is well adaptive to the variation of workload and achieves workload balance with less VMs.

Minimizing Resource Cost for Camera Stream Scheduling in Video Data Center

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.

Online multi-objective optimization for live video forwarding across video data centers

The proliferation of video surveillance has led to surveillance video forwarding services becoming a basic server in video data centers. End users in diverse locations require live video streams from the IP cameras through the inter-connected video data centers. Consequently, the resource scheduler, which is set up to assign the resources of the video data centers to each arriving end user, is in urgent need of achieving the global optimal resource cost and forwarding delay. In this paper, we propose a multi-objective resource provisioning (MORP) approach to minimize the resource provisioning cost during live video forwarding. Different from existed works, the MORP optimizes the resource provisioning cost from both the resource cost and forwarding delay. Moreover, as an approximate optimal approach, MORP adaptively assigns the proper media servers among video data centers, and connects these media servers together through network connections to provide system scalability and connectivity. Finally, we prove that the computational complexity of our online approach is only O(log(|U|)) (|U| is the number of arrival end users). The comprehensive evaluations show that our approach not only significantly reduces the resource provisioning cost, but also has a considerably shorter computational delay compared to the benchmark approaches.

Model secret sharing schemes in systems transmit video

This paper proposes two embodiments secret sharing schemes on non-equal shares with essentially a small part of the secret to securely storage and processing of video data centers, which are based on RGB-representation (abbreviation round English words Red, Blue, Green - Red , green, blue) of each video frame. For each of the proposed scheme is made software implementation in the programming environment Borland C + + Builder 6.

Understanding Internet Video sharing site workload: A view from data center design

Internet Video sharing sites, led by YouTube , have been gaining popularity in a dazzling speed, which also brings massive workload to their service data centers. In this paper we analyze Yahoo! Video, the 2nd largest U.S. video sharing site, to understand the nature of such unprecedented massive workload as well as its impact on online video data center design. We crawled the Yahoo! Video web site for 46 days. The measurement data allows us to understand the workload characteristics at different time scales (minutes, hours, days, weeks), and we discover interesting statistical properties on both static and temporal dimensions of the workload including file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we further extend our understanding on the measurement data with a virtual design on the workload and capacity management components of a data center assuming the same workload as measured, which reveals key results regarding the impact of workload arrival distribution, Service Level Agreements (SLAs), and workload scheduling schemes on the design and operations of such large-scale video distribution systems.