CC-NIE Integration: Supporting Climate Modeling Over Named Data Networking (NDN)

命名数据网络(NDN)是以信息为中心的网络(ICN)的一个实例。与IP不同，NDN专注于内容(The What)而不是主机(The Where)。在NDN中，用户只需按名称而不是其位置请求内容，网络就可以从任何地方定位和检索签名的内容，包括出版商、网络缓存或附近的用户。通过名称指定内容并检索最近的副本的能力对大数据应用程序非常有利，因为它可以无缝定位相关数据并加快大数据集的检索速度，特别是在协作环境中。在这个项目中，科罗拉多州立大学计算机科学和大气科学系的网络研究人员将共同努力，将NDN与全球云解析模型(GCRM)应用程序集成在一起。GCRM使用足够精细的网格来模拟全球大气，以粗略地解析较大的单个云。此应用程序通常会生成TB到PB级的数据集。GCRM目前正在美国和国外的几个建模中心开发。项目贡献包括通过与文件系统的集成、版本控制、设计适当的名称空间以促进无缝数据发现、构建高效的协作和共享环境以及确保数据完整性，将NDN与研究人员的工作流程无缝集成。为了确保顺利过渡到新环境，该项目建立了现有格式和适用于NDN的格式之间的翻译程序。仪表式NDN路由器部署在三个园区，以支持与研究人员在持续反馈循环中进行测试和参与，以改进网络和工具。

Named Data Networking (NDN) is an instance of Information Centric Networking (ICN). Unlike IP, NDN focuses on content (the what) rather than hosts (the where). In NDN users simply request content by name rather than its location, and the network locates and retrieves signed content from anywhere, including the publisher, a network cache or a nearby user. The ability to specify content by name and retrieve the nearest copy is of great benefit to Big Data applications by seamlessly locating related data and speeding up retrieval of large datasets, especially in collaborative environments.In this project, networking researchers at the Computer Science and Atmospheric Sciences departments at Colorado State University will work together to integrate NDN with the Global Cloud Resolving Model (GCRM) application. GCRM simulates the global atmosphere using a grid fine enough to crudely resolve the larger individual clouds. This application typically generates terabyte to petabyte scale datasets. GCRMs are currently under development at several modeling centers in the U.S. and abroad.Project contributions include a seamless integration of NDN with researchers' work flow through integration with the file system, versioning control, the design of appropriate name spaces to facilitate seamless data discovery, building efficient collaboration and sharing environments and ensuring data integrity. To ensure smooth transition to the new environment, the project builds translators between existing and NDN-appropriate formats. Instrumented NDN routers are deployed across three campuses to support testing and engagement in a continuous feedback loop with researchers to improve the network and the tools.