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Interaction of AQM schemes and adaptive streaming with Internet traffic on access networks

机译：AQM方案和自适应流与接入网络上的Internet流量的交互

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We tested AQM techniques on their ability to influence end-user QoE, especially HTTP Adaptive Streaming (HAS) based video traffic on fixed access networks. Using ns-2 we built a set of simulation scenarios for realistic Internet traffic. Based on data from recent Internet measurements, there are three major types of traffic flowing through the Internet: HTTP web traffic; HAS video traffic and progressive download (PD) video traffic. We modeled HTTP web traffic using Internet statistics published by Google. We generated dynamic HAS traffic by implementing a HAS client. We modeled PD video traffic using statistics from Youtube. We used published research to convert data from simulation traces into QoE metrics. We used realistic mixes of the three types of traffic with different loading conditions to test Random Early Detection (RED), CoDel and Tail-drop queuing on DSL access networks. We also tested various options in HAS for their impact on its QoE. Most AQM configurations improved the QoE metrics of HAS and web traffic. They improved fairness among HAS streams, avoided HAS underflow in a majority of the cases where it would occur with Tail-drop, and tangibly reduced the latency for web page downloads. Sensitivity analysis with RED parameters confirmed that the improved QoE of HAS and web traffic was relatively insensitive to the exact RED parameter set.
机译：我们测试了AQM技术影响最终用户QoE的能力，尤其是固定接入网络上基于HTTP自适应流（HAS）的视频流量。使用ns-2，我们为现实的Internet流量构建了一组模拟方案。根据最近的Internet度量数据，流经Internet的流量主要分为三种：HTTP Web流量; HTTP Web流量; HTTP Web流量; HTTP Web流量。 HAS视频流量和渐进式下载（PD）视频流量。我们使用Google发布的互联网统计信息对HTTP网络流量进行了建模。通过实现HAS客户端，我们生成了动态HAS流量。我们使用Youtube的统计数据对PD视频流量进行了建模。我们使用已发表的研究成果将数据从模拟轨迹转换为QoE指标。我们使用了三种流量在不同负载条件下的真实组合，以测试DSL接入网络上的随机早期检测（RED），CoDel和尾部丢弃队列。我们还测试了HAS中的各种选项对其QoE的影响。大多数AQM配置改进了HAS和Web流量的QoE指标。它们改善了HAS流之间的公平性，在大多数情况下避免了HAS下溢（出现尾部拖尾的情况），并切实减少了网页下载的延迟。使用RED参数进行的敏感性分析证实，改进的HAS和网络流量的QoE对精确的RED参数集相对不敏感。

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