CC*IIE Engineer: A Software-Defined Campus Network for Big-Data Sciences

CC*IIE 工程师：面向大数据科学的软件定义校园网络

• 批准号: 1440750
• 负责人: Jennifer Rexford
• 金额: $ 39.98万
• 依托单位: Princeton 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: 2014-09-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440750&HistoricalAwards=false
• 关键词: CC; IIE; Engineer; Software; Defined

Scientific researchers on university campuses create, analyze, visualize, and share large and diverse datasets from experimental devices like brain scanners, particle colliders, and genome sequencers. However, these "big data" applications place strain on traditional campus networks, due to rapidly increasing volumes of data, the need for either predictably low latency (to adapt experiments in real time) or high throughput (to transfer large data sets between locations), and sophisticated access-control policies (to protect the privacy of human subjects). To enable the next wave of scientific advances, university campuses must find effective ways to meet these challenging demands, at reasonable cost. The emerging technology of Software-Defined Networking (SDN) lowers the barrier to innovation in network management, and can substantially reduce cost through (i) inexpensive commodity network switches, (ii) greater automation of network configuration, and (iii) novel network-management applications that optimize bandwidth usage. Yet, existing innovation in SDN focuses primarily on the needs of commercial cloud providers, rather than the unique requirements of university campuses and scientific researchers. Princeton University is creating a software-defined campus network that can enable the next generation of data-driven scientific research. The initiative brings together big-data science researchers, computer scientists who are experts in SDN, and the campus Office of Information Technology. Princeton is deploying an open-source SDN platform for monitoring and configuring the network, conducting trials of new ways to support big-data applications, and bridging with the larger community, on and off campus, to support the sharing of scientific data, SDN software, and operational experiences.

大学校园里的科学研究人员创建、分析、可视化并共享来自脑扫描仪、粒子对撞机和基因组测序仪等实验设备的大量不同数据集。然而，这些“大数据”应用给传统校园网带来了压力，因为数据量迅速增加，需要可预测的低延迟（适应实时实验）或高吞吐量（在不同地点之间传输大数据集），以及复杂的访问控制策略（保护人类受试者的隐私）。为了推动下一波科学进步，大学校园必须找到有效的方法，以合理的成本来满足这些具有挑战性的要求。软件定义网络（SDN）的新兴技术降低了网络管理创新的障碍，并且可以通过(i)廉价的商品网络交换机，（ii）更大程度的网络配置自动化，以及（iii）优化带宽使用的新型网络管理应用程序大幅降低成本。然而，现有的SDN创新主要集中在商业云提供商的需求上，而不是大学校园和科研人员的独特需求。普林斯顿大学正在创建一个软件定义的校园网，以实现下一代数据驱动的科学研究。该计划汇集了大数据科学研究人员、SDN专家计算机科学家和校园信息技术办公室。普林斯顿大学正在部署一个开源SDN平台，用于监控和配置网络，进行支持大数据应用的新方法的试验，并与校园内外的更大社区建立桥梁，以支持科学数据、SDN软件和运营经验的共享。