NeTS: Small: Tools for Large-Scale Network Testbed Experimentation

NeTS：小型：用于大规模网络测试台实验的工具

• 批准号: 1813729
• 负责人: Violet Syrotiuk
• 金额: $ 54.8万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813729&HistoricalAwards=false
• 关键词: NeTS; Small; Tools; Large; Scale

Over the last decade, the use of wireless Internet-connected devices has nearly doubled in the U.S., with users fueling new socially useful applications. Despite being expected to behave predictably under a wide set of circumstances, these engineered networks have complex behaviors, exhibiting characteristics not easily studied using traditional techniques. Experimentation is one way to improve our understanding. Among the many parameters of the network, the devices, and the environment, a necessary first step in experimentation is screening to select those that significantly impact performance for follow-on study. This project contributes to the national interest by developing tools integrated with test-beds to address grand challenges in wireless networks.The minimum requirements for screening in this setting are captured in a locating array (LA). LAs exhibit logarithmic growth in the number of parameters, making practical the consideration of an order of magnitude more parameters in experimentation. The structure of the LA and the analysis to recover the significant parameters and two-way interactions impacting the measured performance are tightly coupled. While the basic requirements of screening are known, the basic requirements of recovery are not well understood. If balance is required, then the Rao bound applies, and the array size must grow polynomially in the number of parameters and two-way interactions. Approaches include either making the recovery algorithm work with the lack of balance in the LA, or making the LA more suitable for recovery. Practical networking applications motivate new structure on the LA that in turn impacts recovery. The goals of this project are to produce tools integrated with testbeds (1) to generate and validate screening designs for large-scale test-bed experimentation, and (2) for their subsequent analysis. This project advances research in two primary areas: (1) Understanding the requirements for recovery, and the trade-offs in array structure and recovery, thus helping to build a bridge between novel LAs and traditional screening designs. (2) The effective use of screening designs requires the deployment of tools to support them. Novel open source tools to construct, verify, and analyze LAs will be developed and integrated in a wireless network test-bed. The tools will be made available in a community wide repository, equipped with documentation and training materials, including steps to integrate them with a variety of test-beds.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

过去十年，美国无线互联网连接设备的使用量几乎翻了一番，用户催生了新的社交应用程序。尽管预计这些工程网络在多种情况下都会表现出可预测的行为，但它们具有复杂的行为，表现出使用传统技术不易研究的特征。实验是提高我们理解的一种方法。在网络、设备和环境的众多参数中，实验必要的第一步是筛选，选择那些对后续研究性能有显着影响的参数。该项目通过开发与测试台集成的工具来解决无线网络中的巨大挑战，为国家利益做出贡献。在定位阵列（LA）中捕获了这种情况下筛选的最低要求。 LA 的参数数量呈对数增长，使得在实验中考虑更多数量级的参数变得可行。 LA 的结构和恢复影响测量性能的重要参数和双向相互作用的分析是紧密耦合的。虽然筛查的基本要求是已知的，但康复的基本要求尚不清楚。如果需要平衡，则应用 Rao 界限，并且数组大小必须随着参数数量和双向交互的数量呈多项式增长。方法包括使恢复算法在 LA 缺乏平衡的情况下工作，或者使 LA 更适合恢复。实际的网络应用激发了洛杉矶的新结构，进而影响恢复。该项目的目标是生产与测试台集成的工具（1）以生成和验证大规模测试台实验的筛选设计，以及（2）用于后续分析。该项目推进了两个主要领域的研究：(1) 了解回收率的要求以及阵列结构和回收率的权衡，从而有助于在新型 LA 和传统筛选设计之间架起一座桥梁。 (2) 筛选设计的有效使用需要部署支持它们的工具。用于构建、验证和分析 LA 的新型开源工具将被开发并集成到无线网络测试台中。这些工具将在社区范围内的存储库中提供，并配备文档和培训材料，包括将它们与各种测试台集成的步骤。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，被认为值得支持。智力价值和更广泛的影响审查标准。

项目成果

New bounds on the maximum size of Sperner partition systems

Sperner 分区系统最大大小的新限制

• DOI: 10.1016/j.ejc.2020.103165
• 发表时间: 2020-12
• 期刊: European Journal of Combinatorics
• 影响因子: 1
• 作者: Chang, Yanxun;Colbourn, Charles J.;Gowty, Adam;Horsley, Daniel;Zhou, Junling
• 通讯作者: Zhou, Junling

Detecting Arrays for Main Effects

检测数组的主效应

• DOI: 10.1007/978-3-030-21363-3_10
• 发表时间: 2019-06-30
• 作者: C. Colbourn;V. Syrotiuk
• 通讯作者: V. Syrotiuk

In-Parameter-Order strategies for covering perfect hash families

用于覆盖完美哈希族的参数顺序策略

• DOI: 10.1016/j.amc.2022.126952
• 发表时间: 2022-05
• 期刊: Applied mathematics and computation
• 影响因子: 4
• 作者: Wagner, Michael;Colbourn, Charles J.;Simos, Dimitris E
• 通讯作者: Simos, Dimitris E

Robustness of Recovery in Locating Array-based Screening Experiments

基于定位阵列的筛选实验中恢复的稳健性

• 发表时间: 2019-01
• 期刊: Spring Simulation Multiconference
• 作者: Seidel, S. A.;Colbourn, C. J.;Syrotiuk, V.R.
• 通讯作者: Syrotiuk, V.R.

Partitioned Search with Column Resampling for Locating Array Construction

通过列重采样进行分区搜索以定位数组构建

• DOI: 10.1109/icstw.2019.00056
• 发表时间: 2019-04-22
• 期刊: 2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)
• 作者: Erin Lanus;C. Colbourn;D. Montgomery
• 通讯作者: D. Montgomery