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首页> 外文期刊>Proceedings of the Workshop on Principles of Advanced and Distributed Simulation >Stochastic Process Models for Packet/Analytic-Based Network Simulations
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Stochastic Process Models for Packet/Analytic-Based Network Simulations

机译：用于基于分组/分析的网络仿真的随机过程模型

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摘要

WE present our preliminary work that develops a new approach to hybrid packet/analytic network simulations for improved network simulation fidelity, scale, and simulation efficiency. Much work in the literature addresses this topic, including [10] [11] [8] [12] [13] and others. Current approaches rely upon models, which we refer to in this paper as Deterministic Fluid Models [9] [12], to address the analytic modeling aspects of these hybrid simulations. Instead we draw upon an extensive literature on stochastic models of queues and (eventually) networks of queues to implement a hybrid stochastic model/packet network simulation. We will refer to our approach as Stochastic Fluid Models throughout this paper. We outline our approach, present test cases, and present simulation results comparing the measured queue metrics from our approach for hybrid simulation to those of a deterministic fluid model hybrid simulation and a full packet-level simulation. We also discuss plans for future areas of research on this approach.
机译：我们介绍了我们的初步工作，该工作为混合数据包/分析网络仿真开发了一种新方法，以提高网络仿真的保真度，规模和仿真效率。文献中有很多工作涉及这个主题，包括[10] [11] [8] [12] [13]等。当前的方法依赖于模型，我们在本文中将其称为确定性流体模型[9] [12]，以解决这些混合模拟的解析模型问题。取而代之的是，我们利用有关队列和（最终）队列网络的随机模型的大量文献来实现混合随机模型/分组网络仿真。在本文中，我们将我们的方法称为随机流体模型。我们概述了我们的方法，给出了测试案例，并给出了仿真结果，比较了从我们用于混合仿真的方法到确定性流体模型混合仿真和完整数据包级仿真的队列度量。我们还将讨论有关此方法的未来研究领域的计划。

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