NeTS: Small: Meta-Networking Research: Analysis, Partitioning, and Mapping Tools for Large Experiments

NeTS：小型：元网络研究：大型实验的分析、分区和映射工具

• 批准号: 1319924
• 负责人: Sonia Fahmy
• 金额: $ 32.58万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319924&HistoricalAwards=false
• 关键词: NeTS; Small; Meta; Networking; Research

This project attacks the pressing research problem of scaling and semi-automating large network experiments. Networking research is technically challenging due to the great scale and heterogeneity of protocols and devices in the Internet that researchers need to experiment with. To address this the project will investigate and tackle the problems of resource limitations and experimental artifacts of the testing platforms and scaling techniques. Work to date has either focused on control plane protocols (e.g., routing simulators), or solely considered the data plane. This project's goal is to conduct experiments that are joint control plane and data plane at a scale not previously possible. The project project includes three complementary efforts to address experimentation scale challenges by designing:(1) Experiment Mapping Tools and Taxonomy: The project will design a general framework, taxonomy, and a set of tools to bridge the current gap between testbed users and large-scale testbed experiments that use multiple scaling techniques. The user can supply hints on desired fidelity of different components, and these will be used to determine a high fidelity mapping for the experiment.(2) Experiment Analysis and Partitioning Tools: The project will design methods to model complex dependencies between components of a large-scale experiment to facilitate planning and mapping. These models may also allow partitioning the large experiment into maximally independent smaller experiments that can be sequentially executed to mimic the large experiment.(3) Applications to Case Studies: The project will use a range of large experiments as applications, focusing on problematic experiments including (a) experiments to understand the effect of misconfigurations, attacks, and defenses on Internet infrastructure (e.g., scalability of RPKI, effect of worms or DDoS on BGP, BGP policy conflicts), (b) experiments for anomaly detection, and (c) experiments with cloud computing.The research will help identify and deploy scalable protocols that will enable the Internet to securely accommodate increased traffic volumes. Impacts of the research include the development and public dissemination of general-purpose experimental tools, large-scale testing techniques, methodologies for the use of testing frameworks, and related graduate-level courseware. The PI will undertake significant outreach efforts to simulation and testbed teams, e.g., DETER/Emulab, GENI, AutoNetkit, ns-3, and to industry. The PI will actively involve undergraduate and graduate students from under-represented minority groups in computer science in the research and educational efforts, and will organize a DIMACS workshop on project topics.

该项目攻击了缩放和半自动化大型网络实验的紧迫研究问题。 由于研究人员需要尝试的互联网方案和设备的规模和异质性的巨大规模和异质性，在技术上在技术上具有挑战性。为了解决这个问题，该项目将调查和解决测试平台和扩展技术的资源限制和实验工件问题。迄今为止的工作要么集中在控制平面协议（例如路由模拟器）上，要么仅考虑了数据平面。该项目的目标是进行实验，这些实验是以前不可能的规模进行关节控制平面和数据平面。 该项目项目包括通过设计来解决实验量表挑战的三项补充工作：（1）实验映射工具和分类法：该项目将设计一个通用框架，分类法和一套工具，以弥合使用多个尺度技术的大规模测试用户与大规模测试型实验之间的当前差距。用户可以提供有关不同组件所需保真度的提示，这些提示将用于确定实验的高保真映射。（2）实验分析和分区工具：该项目将设计方法，以建模大型实验组件之间的复杂依赖性，以促进计划和映射。 These models may also allow partitioning the large experiment into maximally independent smaller experiments that can be sequentially executed to mimic the large experiment.(3) Applications to Case Studies: The project will use a range of large experiments as applications, focusing on problematic experiments including (a) experiments to understand the effect of misconfigurations, attacks, and defenses on Internet infrastructure (e.g., scalability of RPKI, effect of BGP上的蠕虫或DDOS，BGP政策冲突），（b）进行异常检测的实验，以及（c）对云计算的实验。该研究将有助于识别和部署可扩展的协议，以使互联网能够安全地适应流量增加。这项研究的影响包括通用实验工具的开发和公共传播，大规模测试技术，用于使用测试框架的方法和相关研究生级课程的方法。 PI将进行大量的外展工作，以模拟和测试培养的团队，例如DeTer/Emulab，Geni，Autonetkit，NS-3和行业。 PI将积极涉及来自代表性不足的少数群体的本科生和研究生在研究和教育工作中，并将组织有关项目主题的DIMACS研讨会。