Collaborative Research: NeTS-NBD: A Revolutionary 4D Approach to Network-Wide Control and Management

合作研究：NetS-NBD：革命性的 4D 网络范围控制和管理方法

• 批准号: 0519885
• 负责人: Jennifer Rexford
• 金额: $ 50万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0519885&HistoricalAwards=false
• 关键词: Collaborative; Research; NeTS; NBD; Revolutionary

IP networking is a spectacular success, catalyzing the diffusion of data networking across academic institutions, governments, businesses, and homes, world-wide. Yet, despite the fundamental importance of this infrastructure, today's networks are surprisingly fragile and increasingly difficult to configure, control, and maintain. Using a clean-slate approach, the research team will explore a number of fundamental questions related to network control and management. The focus of the research agenda is on IP (layer-3) networks, though the principal investigators (PIs) intend to create networking primitives and services that apply equally well to other technologies, such as layer-2 networks (e.g., Ethernet networks). The starting point for the work is a small set of principles, guiding the control of the network: network-wide views, network-level objectives, and direct control. These principles lead the PIs to a refactoring of network functionality into four components---the data, discovery, dissemination, and decision planes. Via this architecture, which the PIs term the 4D approach to network control and management, the team intends to create, prototype, and demonstrate breakthrough mechanisms that will dramatically simplify and strengthen data networking.INTELLECTUAL IMPACT: The proposed research will address fundamental questions that are key to improving IP control and management: How to go from networks that blend decision logic with specific protocols and mechanisms to an architecture that abstracts and isolates the decision logic and admits a range of efficient implementations? How to go from networks that consist of numerous uncoordinated, error-prone mechanisms, to ones where the low-level mechanisms are driven in a consistent manner by network-level objectives? How to go from networks where people set parameters (twist knobs), hoping to coax the system to reach a desired state, to one where network designers can directly express controls that automatically steer the system toward the desired state? How to go from networks where human administrators leverage network-wide views and box-level capabilities at slow timescales in decision-support systems, to one where the network itself leverages this information in real time?BROADER IMPACT: The proposed research seeks to produce fundamental knowledge that will advance the state-of-the-art in large-scale network architecture, control and management. It intends to lay the groundwork for data networks (in academic campuses, data centers, enterprises, metro areas, backbones) that are more robust, more evolvable, and less prone to security breaches. The involvement of industrial partners in this project will accelerate the transfer of the research results into operation.The general educational impacts are in the training of students, postdocs and researchers, allowing them to cross boundaries between theoretical and system-oriented research. It offers students the unique opportunity to pursue systems work, guided by a deep understanding of fundamental principles, and to use their knowledge creatively to conceive and design innovative software tools and systems. The research team will use existing institutional programs to recruit and involve students from underrepresented groups into the research program from its earliest stages. The results of the research project will be integrated into the undergraduate and graduate computer science program. The software tools to be developed will provide the basis for class projects in the graduate-level courses of the participating institutions. The PIs will create graduate-level courses based on the project's research priorities and goals. The findings will also be used as case studies in undergraduate studies to enhance students' understanding of the main architectural design challenges and control issues of large-scale networks

IP网络取得了巨大的成功，促进了数据网络在全球学术机构、政府、企业和家庭中的传播。 然而，尽管这种基础设施至关重要，但今天的网络却出奇地脆弱，越来越难以配置、控制和维护。研究团队将采用全新的方法，探索与网络控制和管理相关的一些基本问题。 研究议程的重点是IP（第三层）网络，尽管主要研究人员（PI）打算创建同样适用于其他技术的网络原语和服务，例如第二层网络（例如，以太网网络）。 工作的出发点是一组指导网络控制的原则：网络范围的视图，网络级的目标和直接控制。 这些原则引导PI将网络功能重构为四个组件--数据、发现、传播和决策平面。通过这种架构，PI称之为网络控制和管理的4D方法，该团队打算创建、原型化和演示突破性的机制，这些机制将大大简化和加强数据网络。智力影响：拟议的研究将解决对改善IP控制和管理至关重要的基本问题：如何从将决策逻辑与特定协议和机制混合在一起的网络，转变为抽象和隔离决策逻辑并允许一系列有效实现的架构？ 如何从由许多不协调的、容易出错的机制组成的网络，转变为由网络级目标以一致的方式驱动低级机制的网络？ 如何从人们设置参数（旋转旋钮）的网络，希望引导系统达到期望的状态，到网络设计者可以直接表达控制，自动引导系统达到期望的状态？ 如何从人类管理员在决策支持系统中以缓慢的时间尺度利用网络范围的视图和框级功能的网络，发展到网络本身以真实的时间利用这些信息的网络？更广泛的影响：拟议的研究旨在产生基础知识，将推进国家的最先进的大规模网络架构，控制和管理。 它旨在为数据网络（在校园，数据中心，企业，城市地区，骨干网）奠定基础，这些网络更强大，更可发展，更不容易受到安全漏洞的影响。工业合作伙伴的参与将加速研究成果的转化。一般的教育影响是学生，博士后和研究人员的培训，使他们能够跨越理论和系统导向研究之间的界限。它为学生提供了一个独特的机会来追求系统工作，通过对基本原理的深刻理解，并创造性地利用他们的知识来构思和设计创新的软件工具和系统。 研究团队将利用现有的机构计划，从最早的阶段招募和参与代表性不足的群体的学生进入研究计划。 该研究项目的结果将被整合到本科和研究生计算机科学课程。将开发的软件工具将为参与机构的研究生课程中的课堂项目提供基础。 PI将根据项目的研究重点和目标创建研究生课程。研究结果也将作为本科生学习的案例研究，以增强学生对大型网络的主要架构设计挑战和控制问题的理解