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

合作研究：NeTS-NBD：网络范围控制和管理的革命性 4D 方法。

• 批准号: 0519728
• 负责人: Rami Melhem
• 金额: --
• 依托单位: University of Pittsburgh
• 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=0519728&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世界学术机构、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，但当今的网络却出奇地脆弱，并且越来越难以配置、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 3 层）网络，尽管主要研究者 (PI) 打算创建同样适用于其他技术（例如第 2 层网络（例如以太网））的网络原语和服务。 这项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、网络级目标和直接控制。 这些原则引导 PI 将网络功能重构为四个部分：数据、发现、传播和决策平面。通过这种 PI 称之为网络控制和管理 4D 方法的架构，该团队打算创建、原型化和演示突破性机制，从而极大地简化和加强数据网络。 学术影响：拟议的研究将解决对于改进 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的网络转变为抽象和隔离决策逻辑和机制的架构。 承认一系列有效的实施？ 如何从由众多不协调、容易出错的机制组成的网络转变为低层机制由网络层目标以一致的方式驱动的网络？ 如何从人们设置参数（扭转旋钮）以希望引导系统达到所需状态的网络，转变为网络设计者可以直接表达控制以自动引导系统达到所需状态的网络？ 如何从人类管理员在决策支持系统中以较慢的时间尺度利用全网视图和盒级功能的网络，转变为网络本身实时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，以推动大规模网络架构、控制和管理的最先进水平。 它旨在为更强大、更易于发展且不易出现安全漏洞的数据网络（学术园区、数据中心、企业、城域、骨干网）奠定基础。工业合作伙伴参与该项目将加速研究成果转化为应用。一般教育影响在于对学生、博士后和研究人员的培训，使他们能够跨越理论研究和系统导向研究之间的界限。它为学生提供了独特的机会，在对基本原理的深刻理解的指导下进行系统工作，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项目招募代表性不足群体的学生，并让他们从最早阶段就参与到研究项目中。 该研究项目的成果将被纳入本科生和研究生计算机科学项目中。待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水平的课程。研究结果还将用作本科学习的案例研究，以增强学生对大规模网络的主要架构设计挑战和控制问题的理解