Stochastic Upper and Lower Bounds for General Markov Fluids

机译：一般马尔可夫流体的随机上下界

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Promising perspectives of a hypothetical 'Tactile Internet', or 'Internet at the speed of light', whereby network latencies become imperceptible to users, have (again) triggered a broad interest to understand and mitigate Internet latencies. In this paper we revisit the queueing analysis of the versatile Markov Fluid traffic model, which was mainly investigated in the 1980-90s, yet with low accuracy. We derive upper bounds on the tail distribution of the queue size, which improve state-of-the-art results by an exponential factor O (κn) in a special case, where 0

机译：假想的“触觉互联网”或“光速互联网”的有前途的观点（使网络延迟对于用户而言不可察觉）再次引起了人们广泛的兴趣，以了解和缓解互联网延迟。在本文中，我们将对通用马尔可夫流体交通模型的排队分析进行回顾，该模型主要在1980-90年代进行了研究，但准确性较低。我们得出队列大小的尾部分布的上限，在特殊情况下，该乘数将指数因子O（κn）改进为最新水平，其中0

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《International Teletraffic Congress》|2016年|184-192|共9页
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Florin Ciucu; Felix Poloczek; Jens Schmitt;
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Markov processes; Internet; Queueing analysis; Delays; Calculus; Bandwidth;

机译：马尔可夫过程;互联网;排队分析;延迟;微积分;带宽;

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Stochastic Upper and Lower Bounds for General Markov Fluids

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