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

合作研究：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: 0709081
• 负责人: Kevin Jeffay
• 金额: $ 47.36万
• 依托单位: University of North Carolina at Chapel Hill
• 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 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0709081&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.

网络研究长期以来依赖模拟作为证明所提出的协议和机制有效性的主要工具。通常，人们使用广泛使用的 ns-2 模拟器等软件来模拟网络硬件和软件。实验通过使用 ftp 或 Web 浏览器等应用程序模拟给定用户群对网络的使用情况来进行。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。为了执行真实的网络模拟，需要一种流量生成器，能够以“看起来像”的闭环方式生成真实的合成流量。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，网络技术经常使用与实际链路上看到的流量关系未知的临时工作负载进行评估，因此引发了评估结果可信度的问题。该项目是一项协作项目，旨在为实验网络研究社区开发合成流量生成资源。该资源包括 (1) 用于 ns-2、ns-3 和 GTNets 软件模拟器以及基于 Linux 和 BSD 的测试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，该流量在统计上与各种网络链路（包括校园网络、广域骨干网、企业内部网、无线网络等）上发现的流量相当，以及 (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接上的流量经验模型，并使用这些模型来驱动合成流量生成过程。