CRII: NeTS: Characterizing, Quantifying and Modeling Network Complexity

CRII：NeTS：网络复杂性的表征、量化和建模

• 批准号: 1660569
• 负责人: Xin Sun
• 金额: $ 13.43万
• 依托单位: Ball State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1660569&HistoricalAwards=false
• 关键词: CRII; NeTS; Characterizing; Quantifying; Modeling

Networks with higher degrees of complexity generally require more manual intervention to manage, are more difficult to reason about, and are more prone to human errors. Despite its critical importance, complexity remains one of the least understood aspects of a network, and has not been well characterized or modeled. There is a general perception that for the same target network multiple designs often exist to meet the same set of operational objectives (e.g., security and resiliency requirements), and that some designs could be significantly easier to manage than others; however the decisions on complexity are typically made subjectively or via qualitative terms. This project aims at developing a fundamentally new understanding of, and metrics and models for, network complexity. The project studies how a network's design characteristics impact the complexity in its management, through a novel white-box approach that involves reverse-engineering the network design from device configurations, identifying design patterns that differ from conventional wisdom, and iterative discussions with network operators to capture perceived complexity. A suite of bottom-up metrics will be developed to accurately quantify the complexity of a given design. These metrics enable comparisons of alternative design proposals and "what-if" analyses of how design changes might impact the management complexity. A top-down framework will be developed to model the interplay among network design, operational objectives, and the resulting management complexity. The framework formally abstracts the objectives of a given design task thus allowing reasoning about whether and how a combination of design primitives will meet those objectives, decomposes the design task into its constituent primitives, and applies the metrics to quantify the complexity of individual primitives.If successful this project will advance the state-of-the-art in understanding, quantifying, and modeling network complexity. The modeling framework enables complexity-aware top-down network design by guiding the search through the design space. The insights obtained from this project may inform the design of future network architectures, management applications, and configuration languages. The project's PI will actively interact with the research community, operator community, and Internet Engineering Task Force to disseminate the research findings. Results from the project will be incorporated into graduate-level network management classes. As Florida International University is a designated Hispanic-serving institution, this project will serve as a vehicle to advance the involvement of underrepresented minorities in computer science research.

复杂程度较高的网络通常需要更多的人工干预来管理，更难以推理，并且更容易出现人为错误。尽管复杂性至关重要，但它仍然是网络中最不为人所知的方面之一，并且尚未得到很好的表征或建模。普遍的看法是，对于同一目标网络，常常存在多种设计以满足同一组操作目标（例如，安全性和弹性要求），并且一些设计可能比其他设计更容易管理;然而，关于复杂性的决定通常是主观地或通过定性术语做出的。该项目旨在对网络复杂性进行全新的理解，并制定度量和模型。该项目研究了网络的设计特征如何影响其管理的复杂性，通过一种新颖的白盒方法，包括从设备配置逆向工程网络设计，识别不同于传统智慧的设计模式，以及与网络运营商的迭代讨论，以捕捉感知的复杂性。将开发一套自下而上的指标，以准确量化给定设计的复杂性。这些指标可以比较备选设计方案，并对设计变更如何影响管理复杂性进行“假设”分析。将开发一个自上而下的框架，以模拟网络设计、运营目标和由此产生的管理复杂性之间的相互作用。该框架正式抽象了一个给定的设计任务的目标，从而允许推理是否以及如何组合的设计原语将满足这些目标，分解成其组成primitive的设计任务，并适用于度量，以量化的复杂性，个别primities.If成功的这个项目将推进国家的最先进的理解，量化和建模网络的复杂性。建模框架通过引导搜索通过设计空间，使复杂性感知自顶向下的网络设计。从这个项目中获得的见解可能会通知未来的网络架构，管理应用程序和配置语言的设计。该项目的PI将积极与研究界，运营商社区和互联网工程任务组进行互动，以传播研究成果。该项目的成果将纳入研究生级别的网络管理课程。由于佛罗里达国际大学是一个指定的西班牙裔服务机构，该项目将作为一个工具，以促进在计算机科学研究的代表性不足的少数民族的参与。