论文标题
随机网络计算与Martingales的局部应用
Stochastic Network Calculus with Localized Application of Martingales
论文作者
论文摘要
随机网络演算是一种概率方法，用于计算网络中的性能界限，例如端到端延迟。它依赖于使用（确定性）网络演算的形式主义对随机过程的分析。但是，与确定性理论不同，与仿真相比，计算的边界通常非常宽松。这主要是由于大量使用了布尔的不平等。另一方面，基于martingales的分析可以达到紧密的界限，但直到现在，它们尚未应用于服务器序列。在本文中，我们通过将这种Martingale分析与最新的随机网络计算结果相结合的基于付费单互联网元素的属性的结果来提高随机网络演算的准确性，这是从确定性网络计算中众所周知的。我们展示了一类非平凡的网络，可以从此分析中受益，并将我们的界限与模拟进行比较。
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.
