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首页> 外文期刊>IEEE transactions on multimedia >Resource Allocation With Video Traffic Prediction in Cloud-Based Space Systems
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Resource Allocation With Video Traffic Prediction in Cloud-Based Space Systems

机译：基于云的空间系统中具有视频流量预测的资源分配

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This paper considers the resource allocation problems for video transmission in space-based information networks. The queueing system analyzed in this study is constituted by multiple users and a single server. The server is operated as a cloud that can sense the traffic arrivals to each user's queue and then allocates the transmission resource and service rate for users. The objectives are to make configurations over time to minimize the time average cost of the system, and to minimize the waiting time of packets after they enter the queue. Meanwhile, the constraints on the queue stability of the system must be satisfied. In this paper, we introduce a predictive backpressure algorithm, which considers the future arrivals with a certain prediction window size into the consideration of resource allocation to make decisions on which packets to be served first. In addition, this paper designs a multiresolution wavelet decomposition-based backpropagation network for the prediction of video traffic, which exhibits the long-range dependence property. Simulation results indicate that the delay of the queueing system can be reduced through this prediction-based resource allocation, and the prediction accuracy for the video traffic is improved according to the proposed prediction system.
机译：本文考虑了天基信息网络中视频传输的资源分配问题。本研究中分析的排队系统由多个用户和一个服务器组成。服务器作为云运行，可以感知到每个用户队列的流量，然后为用户分配传输资源和服务速率。目标是随着时间进行配置，以最大程度地减少系统的平均时间成本，并最大程度地减少数据包进入队列后的等待时间。同时，必须满足对系统队列稳定性的约束。在本文中，我们介绍了一种预测性背压算法，该算法将具有一定预测窗口大小的将来到达考虑为资源分配的考虑因素，以决定首先要服务哪个数据包。此外，本文设计了一种基于多分辨率小波分解的反向传播网络来预测视频流量，该网络具有远距离依赖性。仿真结果表明，通过这种基于预测的资源分配，可以减少排队系统的延迟，并且根据所提出的预测系统，可以提高视频流量的预测精度。

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