Modeling and Prediction of Software-Defined Networks Performance using Queueing Petri Nets
Modeling and Prediction of Software-Defined Networks Performance using Queueing Petri Nets
复制标题
DOI：
--
复制
发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Piotr Rygielski;M. Seliuchenko;Samuel Kounev
中科院分区：
文献类型：
--
作者：
Piotr Rygielski;M. Seliuchenko;Samuel Kounev
使用各种建模和仿真方法来预测网络性能需要丰富的经验，并且涉及关于每个建模形式主义的许多耗时的手动步骤。Descartes Network Infrastructure（DNI）是一种数据中心网络性能建模方法，它通过提供多种性能模型但只需要使用一种建模语言来解决这一挑战。在本文中，我们彻底扩展了DNI以支持新的网络模式，例如软件定义网络（SDN）和网络功能虚拟化（NFV）。此外，我们还演示了如何使用DNI对基于SDN的网络进行建模，以及如何使用模型到模型转换将其转换为嵌入式Petri网（QPN）。在性能预测精度的分析中，我们表明，自动生成的QPN模型代表异构SDN硬件的性能，最大预测精度误差为12%。
Using various modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. Descartes Network Infrastructure (DNI) is a data center network performance modeling approach that addresses this challenge by offering multiple performance models but requiring to use only a single modeling language. In this paper, we thoroughly extend DNI to support new networking paradigms like, among others, Software-Defined Networking (SDN) and Network-Function Virtualization (NFV). Additionally, we demonstrate how SDN-based networks can be modeled using DNI and how are they transformed later into Queueing Petri Nets (QPN) using a model-to-model transformation. In the analysis of the performance prediction accuracy, we show that automatically generated QPN models represent the performance of heterogeneous SDN hardware with maximal prediction accuracy error of 12%.