CC*IIE Integration: Dynamically Optimizing Research Data Workflow with a Software Defined Science Network

CC*IIE 集成：利用软件定义的科学网络动态优化研究数据工作流程

• 批准号: 1440745
• 负责人: Yang Yang
• 金额: $ 78.86万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440745&HistoricalAwards=false
• 关键词: CC; IIE; Integration; Dynamically; Optimizing

Intelligent management of campus research networks has become a major challenge for many institutions, as their networks grow rapidly in size and complexity in order to meet the demands of on-campus scientists who are conducting research, collaborating with peers, and fulfilling their mission of scientific education. Traditional, static network management approaches are no longer adequate, since they often result in low efficiency, poor usability, and unpredictable network application performance. The goal of this project is to design and deploy a novel intelligent network cyberinfrastructure that greatly expands the ability of scientists to rapidly and efficiently move the large quantities of data required for computation- and data-intensive scientific workflows. To ensure a broad impact, the project includes specific focus on a range of science drivers in diverse fields such as astronomy, climatology, and genomics. The project achieves its goal by leveraging and validating several prior networking research and development efforts. These include Maple, a novel Software Defined Networking (SDN) programming framework developed at Yale, and an Application Layer Traffic Optimization (ALTO) protocol and framework pioneered at Yale and now incorporated in a proposed standard for the Internet by the Internet Engineering Task Force. Maple simplifies network programming for end-to-end, complex, dynamically constructed network services, while ALTO enables network applications to adapt dynamically, according to network states, to deliver network efficiency and application quality of service. In addition, the project builds on prior Yale and NSF investments in high-speed physical network cyberinfrastructure, the widely-adopted InCommon authentication framework, and IPv6 technology.

随着高校科研网络规模和复杂性的迅速增长，高校科研网络的智能化管理已成为高校科研人员开展科研、开展科研合作、完成科研教育任务的重要课题。传统的静态网络管理方法不再适用，因为它们通常导致效率低、可用性差和不可预测的网络应用程序性能。该项目的目标是设计和部署一种新型智能网络网络基础设施，极大地扩展了科学家快速有效地移动计算和数据密集型科学工作流程所需的大量数据的能力。为了确保产生广泛的影响，该项目特别关注天文学、气候学和基因组学等不同领域的一系列科学驱动因素。该项目通过利用和验证几个先前的网络研究和开发工作来实现其目标。其中包括Maple，耶鲁大学开发的一种新颖的软件定义网络（SDN）编程框架，以及耶鲁大学首创的应用层流量优化（ALTO）协议和框架，该协议和框架现已被互联网工程任务组纳入拟议的互联网标准。Maple简化了端到端的、复杂的、动态构建的网络服务的网络编程，而ALTO使网络应用程序能够根据网络状态动态适应，从而提供网络效率和应用程序服务质量。此外，该项目建立在耶鲁大学和美国国家科学基金会之前在高速物理网络网络基础设施、广泛采用的InCommon认证框架和IPv6技术方面的投资基础上。