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Probabilistic Analysis of Resequencing Queue Length in Multipath Packet Data Networks

机译:多路径分组数据网络中重排序队列长度的概率分析

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In multipath packet data networks, packets may reach the receiver out-of-sequence, i.e., packets arrive at the receiver in a sequence different from their egressing order at the transmitter. In practice, however, many applications require an in-sequence packet delivery, meaning that packets need to be delivered to an application on the receiver in their original order at the transmitter. The in-sequence packet delivery is usually implemented through the approach of packet resequencing. In this paper, a multipath data network with packet resequencing is modeled and the asymptotic properties of the steady-state probability distribution of the resequencing queue length are studied. The assumptions used are that the packets sent from the transmitter according to a Poisson process, and the transmission period of a packet along a route follows an exponential distribution. An asymptotic distribution function of the resequencing queue length is derived for a large queue length in the steady state of the network. Numerical and simulation examples are presented to validate the derived result. Through comparisons of large deviation and asymptotic values of the resequencing queue length distribution, we show that the asymptotic result provides a better approximation to the distribution function of the resequencing queue length than the large deviation result reported in the literature.
机译:在多径分组数据网络中,分组可能不按顺序到达接收器,即,分组以与它们在发射器处的输出顺序不同的顺序到达接收器。但是,实际上,许多应用程序需要按顺序发送数据包,这意味着需要在发送器上按原始顺序将数据包发送到接收器上的应用程序。顺序分组传送通常通过分组重排序的方法来实现。本文对具有包重排序的多路径数据网络进行建模,并研究了重排序队列长度的稳态概率分布的渐近性质。所使用的假设是根据泊松过程从发送器发送的数据包,并且数据包沿路由的传输周期遵循指数分布。在网络的稳定状态下,对于较大的队列长度,得出了重新排序队列长度的渐近分布函数。数值和仿真实例验证了所得到的结果。通过对重排序队列长度分布的大偏差和渐近值进行比较,我们发现,与文献中报道的大偏差结果相比,渐近结果对重排序队列长度的分布函数提供了更好的近似值。

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