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A random-access algorithm for data networks carrying high-priority traffic

机译:承载高优先级流量的数据网络的随机访问算法

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摘要

The authors consider data networks carrying mixed traffic, partially low and partially high priority. For such networks, they propose a synchronous limited sensing random access algorithm per channel, which gives a delay advantage to the high-priority traffic. The algorithm basically consists of two dynamically coupled window algorithms, one for the high and one for the low-priority packets. Assuming limit Poisson user models for both the traffic classes, the authors optimize the windows of the mixed algorithm for: (a) throughput maximization under no specific delay constraints, and (b) throughput maximization subject to expected delay constraints for the high-priority traffic. For cases (a) and (b), they study the delay characteristics of the algorithm, for the two classes of data traffic. With appropriate selection of the two window sizes, the algorithm can attain relatively high throughput, while it simultaneously maintains low expected per high-priority packet delays, whose values are only slightly affected by the total traffic rate.
机译:作者考虑了承载混合流量,部分低优先级和部分高优先级的数据网络。对于此类网络,他们提出了每个通道的同步受限感测随机访问算法,该算法为高优先级流量提供了延迟优势。该算法基本上由两种动态耦合的窗口算法组成,一种用于高优先级数据包,另一种用于低优先级数据包。假设两种流量类别都具有Poisson用户模型的极限,作者针对以下因素优化混合算法的窗口:(a)在没有特定延迟约束的情况下使吞吐量最大化,以及(b)在高优先级流量的预期延迟约束下实现吞吐量最大化。对于情况(a)和(b),他们研究了针对两类数据流量的算法的延迟特性。通过适当选择这两个窗口大小,该算法可以获得相对较高的吞吐量,同时每个高优先级数据包延迟保持较低的期望值,其值仅受总流量速率的轻微影响。

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