Stochastic Network Calculus is a probabilistic method to compute performance bounds in networks, such as end-to-end delays. It relies on the analysis of stochastic processes using formalism of (Deterministic) Network Calculus. However, unlike the deterministic theory, the computed bounds are usually very loose compared to the simulation. This is mainly due to the intensive use of the Boole's inequality. On the other hand, analyses based on martingales can achieve tight bounds, but until now, they have not been applied to sequences of servers. In this paper, we improve the accuracy of Stochastic Network Calculus by combining this martingale analysis with a recent Stochastic Network Calculus results based on the Pay-Multiplexing-Only-Once property, well-known from the Deterministic Network calculus. We exhibit a non-trivial class of networks that can benefit from this analysis and compare our bounds with simulation.
翻译:托盘网络计算法是一种概率方法,用来计算网络的性能极限,例如端到端的延迟。 它依靠的是使用( 确定性) 网络计算法的形式主义来分析随机过程。 但是, 与确定性理论不同, 计算性界限通常与模拟相比非常松散。 这主要是由于大量使用布尔的不平等性。 另一方面, 基于马丁堡的分析可以达到严格界限, 但直到现在, 它们还没有应用到服务器的序列中。 在本文中, 我们通过将这一马丁格网络的计算法与基于薪酬- 双向网络微积分属性的最新数字结果相结合, 来提高托盘网络计算法的准确性。 我们从威慑性网络的微积分中可以知道。 我们展示了非三进网络的分类, 可以从这一分析中获益, 用模拟来比较我们的界限。