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Dynamic resource allocation based on online traffic prediction for video streams

机译:基于在线流量预测的视频流动态资源分配

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In this paper, we propose a new dynamic resource allocation (DRA) scheme to support the constantly increasing online video stream traffic, especially high definition (HD) video streams. Our DRA scheme is based on online traffic prediction using seasonal time analysis. Our scheme seeks to provide accurate traffic prediction, to minimize the resource negotiation frequency, and to increase the utilization of the network resources while meeting maximum delay requirements. We validate our approach using various video traces, including our video collection of more than 50 HD video traces. We show through our results that our proposed scheme achieve up to 19.8% improvement in allocating bandwidth for short-length video traces, and up to 25% for long traces compared to the variable step-size adaptive (VSA) algorithm.
机译:在本文中,我们提出了一种新的动态资源分配(DRA)方案,以支持不断增长的在线视频流流量,尤其是高清(HD)视频流。我们的DRA计划基于使用季节性时间分析的在线流量预测。我们的方案旨在提供准确的流量预测,以最小化资源协商频率,并在满足最大延迟要求的同时提高网络资源的利用率。我们使用各种视频轨迹来验证我们的方法,包括我们收集的50多种高清视频轨迹。通过我们的结果,我们发现,与可变步长自适应(VSA)算法相比,我们提出的方案在短视频迹线的带宽分配方面提高了19.8%,在长迹线的带宽分配方面达到了25%。

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