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.

网络研究长期以来一直依赖于模拟作为证明拟议方案和机制有效性的主要工具。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的用户群体模拟网络的使用来进行实验。合成工作负载生成器用于根据应用程序或用户的行为方式的模型将数据注入网络中。在执行现实网络模拟的过程中，需要一个能够以闭环方式生成现实的合成流量的流量生成器，看起来像？在实际网络上找到的流量。不幸的是，网络社区遭受了缺乏适合合成交通生成的验证工具和模型。结果，经常使用与实际链接上的流量的临时工作负载一起评估网络技术，因此提出了一个问题，即评估结果是如何可信的。该项目是为实验性网络研究社区开发合成流量生成资源的协作努力。 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）一组流量分析工具，使研究人员能够在感兴趣的网络链接上生成经验模型，并使用这些模型来推动综合交通生成过程。