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.

校园研究网络的智能管理已成为许多机构的重大挑战，因为它们的网络的规模和复杂性迅速增长，以满足正在进行研究，与同龄人合作以及履行其科学教育使命的校园科学家的需求。传统的静态网络管理方法不再足够，因为它们通常会导致效率低下，可用性差和不可预测的网络应用程序性能。该项目的目的是设计和部署一种新颖的智能网络网络基础结构，该网络基础架构大大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所需的大量数据。为了确保广泛的影响，该项目将专门关注于天文学，气候学和基因组学等各个领域的一系列科学驱动因素。该项目通过利用和验证几项先前的网络研究和开发工作来实现其目标。其中包括Maple，这是在耶鲁大学开发的新型软件定义网络（SDN）编程框架，以及在耶鲁大学开创的应用层流量优化（ALTO）协议和框架，现在由Internet工程工作组纳入Internet标准。 Maple简化了网络编程，用于端到端，复杂，动态构建的网络服务，而Alto使网络应用程序可以动态适应网络效率和应用程序质量。此外，该项目基于耶鲁大学和NSF的高速物理网络网络基础设施，广泛管理的Incommon身份验证框架和IPv6技术的投资。