Collaborative Research: CRI: CRD: Synthetic Traffic Generation Tools and Resources: A Community Resource for Experimental Networking Research

协作研究：CRI：CRD：综合流量生成工具和资源：实验网络研究的社区资源

• 批准号: 0709058
• 负责人: Michele Weigle
• 金额: $ 20.18万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0709058&HistoricalAwards=false
• 关键词: Collaborative; Research; CRI; CRD; Synthetic

Networking research has long relied on simulation as the primary vehicle for demonstrating the effectiveness of proposed protocols and mechanisms. Typically, one simulates network hardware and software in software using, for example, the widely used ns-2 simulator. Experimentation proceeds by simulating the use of the network by a given population of users using applications such as ftp or web browsers. Synthetic workload generators are used to inject data into the network according to a model of how the applications or users behave.In order to perform realistic network simulations, one needs a traffic generator that is capable of generating realistic synthetic traffic in a closed-loop fashion that ?looks like? traffic found on an actual network. Unfortunately, the networking community suffers from a lack of validated tools and models suitable for synthetic traffic generation. As a result, all too often, networking technology is evaluated using ad hoc workloads with an unknown relationship to traffic seen on real links and hence begs the question of how believable the results of the evaluation are.This project is a collaborative effort to develop a synthetic traffic generation resource for the experimental networking research community. The resource consists of (1) synthetic traffic generators for the ns-2, ns-3, and GTNets software simulators, and Linux and BSD-based testbeds, (2) a repository of datasets to be used by the traffic generators to generate traffic that is statistically equivalent to traffic found on a variety of network links including campus networks, wide-area backbone networks, corporate intranets, wireless networks, etc, and (3) a set of traffic analysis tools to enable researchers to generate empirical models of traffic on network links of interest and to use these models to drive the synthetic traffic generation process.

长期以来，网络研究一直依赖于仿真作为证明所提出的协议和机制的有效性的主要工具。通常，人们使用例如广泛使用的ns-2模拟器来模拟软件中的网络硬件和软件。实验通过模拟给定用户群使用ftp或web浏览器等应用程序对网络的使用来进行。合成工作负载生成器用于根据应用程序或用户行为的模型将数据注入网络。为了执行逼真的网络模拟，需要一个能够以闭环方式生成逼真合成流量的流量生成器，该流量生成器？长什么样在实际网络上发现的流量。不幸的是，网络社区缺乏适用于合成流量生成的有效工具和模型。因此，太多的时候，网络技术的评估使用特设的工作负载与一个未知的关系，以流量上看到的真实的links，因此回避了如何可信的评估结果是一个问题。这个项目是一个合作的努力，开发一个综合的流量生成资源的实验网络研究社区。该资源包括（1）用于ns-2、ns-3和GTNet软件模拟器以及基于Linux和BSD的测试平台的合成流量生成器，（2）流量生成器用于生成流量的数据集存储库，该流量在统计上等同于在各种网络链路（包括校园网、广域骨干网、公司内联网、无线网络等）上发现的流量，以及（3）一组流量分析工具，使研究人员能够生成感兴趣的网络链路上的流量的经验模型，并使用这些模型来驱动合成流量生成过程。