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: 0709185
• 负责人: Amin Vahdat
• 金额: $ 6.58万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0709185&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有效性的主要工具。通常，人们使用软件来模拟网络硬件和软件，例如，广泛使用的ns-2模拟器。实验通过模拟给定用户群使用诸如ftp或web浏览器之类的应用程序对网络的使用进行。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。为了进行真实的网络模拟，需要一个流量生成器，它能够以闭环的方式生成真实的合成流量。看起来像吗?在实际网络中发现的流量。不幸的是，网络社区缺乏适合合成流量生成的经过验证的工具和模型。因此，网络技术常常使用与实际链接上看到的流量关系未知的特别工作负载进行评估，从而引发了评估结果可信度如何的问题。该项目是一项协作努力，旨在为实验网络研究社区开发综合流量生成资源。该资源包括：(1)用于ns-2、ns-3和GTNets软件模拟器的合成流量生成器，以及基于Linux和bsd的测试平台；(2)流量生成器使用的数据集存储库，用于生成统计上等同于各种网络链路（包括校园网、广域骨干网、企业内部网、无线网络等）上的流量；(3)一套流量分析工具，使研究人员能够生成感兴趣的网络链路流量的经验模型，并使用这些模型驱动综合流量生成过程。