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Traffic Models for User-Level Performance Evaluation in Data Networks

机译:数据网络中用于用户级性能评估的流量模型

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Traffic modeling is key to the capacity planning of data networks. Usual models rely on the implicit assumption that each user generates data flows in series, one after the other, the ongoing flows sharing equitably the considered backhaul link. We relax this assumption and consider the more realistic case where users may generate several data flows in parallel, these flows having to share the user's access line as well. We derive explicit user-level performance metrics like mean throughput and congestion rate in this context, assuming balanced fair sharing between ongoing flows. These results generalize existing ones in that both match in the limit of an infinite number of access lines.
机译:流量建模是数据网络容量规划的关键。通常的模型基于每个用户依次生成数据流的隐含假设,正在运行的流公平地共享了所考虑的回程链路。我们放宽此假设,并考虑更现实的情况,即用户可能并行生成多个数据流,这些数据流也必须共享用户的访问线路。假设正在进行的流量之间保持公平的公平共享,我们可以得出明确的用户级性能指标,例如在这种情况下的平均吞吐量和拥塞率。这些结果概括了现有技术的局限性,因为它们都在无限数量的访问线路中匹配。

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