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The Effect of Flow Capacities on the Burstiness of Aggregated Traffic

机译:通行能力对交通总量突发性的影响

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摘要

Several research efforts have recently focused on the burstin-ess of Internet traffic in short, typically sub-second, time scales. Some traces reveal a rich correlation structure in those scales, while others indicate uncorrelated and almost exponential interarrivals. What makes the Internet traffic bursty in some links and much smoother in others? The answer is probably long and complicated, as burstiness in short scales can be caused by a number of different application, transport, and network mechanisms. In this note, we contribute to the answer of the previous question by identifying one generating factor for traffic burstiness in short scales: high-capacity flows. Such flows are able to inject large amounts of data to the network at a high rate. To identify high-capacity flows in a network trace, we have designed a passive capacity estimation methodology based on packet pairs sent by TCP flows. The methodology has been validated with active capacity measurements, and it can estimate the pre-trace capacity of a flow for about 80% of the TCP bytes in the traces we analyzed. Applying this methodology to Internet traces reveals that, if a trace includes a significant amount of traffic from high-capacity flows, then the trace exhibits strong correlations and burstiness in short time scales.
机译:最近,一些研究工作集中在短时间内(通常为亚秒级)的Internet流量激增问题上。一些迹线揭示了在这些尺度上的丰富的相关结构,而另一些迹线则表明了不相关且接近指数的到达间隔。是什么使某些链接的互联网流量突然增加,而另一些链接更流畅?答案可能是漫长而复杂的,因为小规模的突发性可能是由许多不同的应用程序,传输和网络机制引起的。在本说明中,我们通过确定短时间内交通突发性的一个生成因素:高容量流量,为前面的问题做出了贡献。这样的流能够以高速率向网络注入大量数据。为了识别网络跟踪中的高容量流,我们基于TCP流发送的数据包对设计了一种被动容量估算方法。该方法已通过活动容量测量得到了验证,并且可以估计我们分析的跟踪中大约80%的TCP字节流的跟踪前容量。将这种方法应用于Internet跟踪会发现,如果跟踪中包含来自高容量流的大量流量,则该跟踪将在短时间内显示出强相关性和突发性。

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