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.

该项目解决了规模化和半自动化大型网络实验的紧迫研究问题。 网络研究在技术上具有挑战性，因为研究人员需要在互联网上进行实验的协议和设备的规模和异构性。为了解决这个问题，该项目将调查和解决资源限制和测试平台和扩展技术的实验工件的问题。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），或者仅考虑数据平面。该项目的目标是进行联合控制平面和数据平面的实验，其规模在以前是不可能的。 该项目包括三个互补的努力，通过设计来解决实验规模的挑战：（1）实验映射工具和分类：该项目将设计一个通用框架，分类和一套工具，以弥合测试台用户和使用多种缩放技术的大规模测试台实验之间的差距。用户可以提供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：该项目将设计方法来模拟大规模实验组件之间的复杂依赖关系，以促进规划和映射。这些模型还可以允许将大型实验划分为最大独立的较小实验，这些较小实验可以顺序执行以模拟大型实验。(3)应用案例研究：该项目将使用一系列大型实验作为应用程序，重点是有问题的实验，包括（a）实验，以了解错误配置，攻击和防御对互联网基础设施的影响（例如，RPKI的可扩展性、蠕虫或DDoS对BGP的影响、BGP策略冲突）、（B）异常检测实验和（c）云计算实验。该研究将有助于识别和部署可扩展的协议，使互联网能够安全地适应增加的流量。研究的影响包括通用实验工具，大规模测试技术，测试框架的使用方法，以及相关的研究生水平的课件的开发和公众传播。PI将对模拟和试验台团队进行重要的外展工作，例如，DETER/Emulab、GENI、AutoNetkit、ns-3和工业。PI将积极参与计算机科学中代表性不足的少数群体的本科生和研究生的研究和教育工作，并将组织一个关于项目主题的DIMACS研讨会。