CC*IIE Networking Infrastructure: Science DMZ for Campus Bridging and Computationally Assisted Research

南达科他州大学的CC*IIE网络基础设施：用于校园桥接和计算辅助研究的科学DMZ项目旨在通过将研究网络流量与传统校园网的摩擦隔离开来，从而改善科学数据的移动。传统的企业网络旨在支持服务于各种组织任务的应用程序，以及保护组织的财务和其他敏感数据的安全措施。其结果是，网络既不是为数据密集型研究的任务而设计的，也不能为其服务。专用研究网络（Science DMZ）支持以下目标：(1)提供对广域网的无摩擦访问；(2)支持从实验数据采集到分析和事后分析的端到端数据流精简化；(3)确保数据传输应用程序的安全性能。科学非军事区实现了多学科合作研究校园，支持多个学科的校园桥梁，包括：粒子物理学、神经科学、材料科学、生物医学研究、生物学和可再生能源。在美国校园网络基础设施计划的大背景下，该项目结合了网络实施和测量以及联合认证方面的国家最佳实践。实施计划包括一个专用的数据传输节点，专门用于实现跨广域网的数据传输。这些目标是通过使用perfSONAR等工具进行网络优化来实现的，这些工具用于监控网络性能。该项目加速了在大数据移动方面采用国家最佳实践，并有助于南达科他州作为EPSCoR州的持续竞争力。

The University of South Dakota's CC*IIE Networking Infrastructure: Science DMZ for Campus Bridging and Computationally Assisted Research project aims to improve scientific data movement by isolating research network traffic from the friction of the traditional campus network. Traditional enterprise networks are designed to support applications serving a variety of organizational missions as well as security measures to protect the organization's financial and other sensitive data. The result is a network that is neither designed for nor capable of serving the mission of data intensive research. The dedicated research network (Science DMZ) supports the following objectives: (1) Provide friction-free access to wide area networks; (2) Support streamlined end-to-end data flows from experimental data acquisition through analysis and post analysis; and (3) Ensure secure performance of data transfer applications.The Science DMZ enables a multidisciplinary collaborative research campus, supporting campus bridging in several disciplines including: particle physics, neuroscience, materials science, biomedical research, biology, and renewable energy. Laid out in the wider context of the USD's Campus Cyberinfrastructure plan, the project incorporates national best practices in network implementation and measurement as well as federated authentication. The implementation plan includes a dedicated Data Transfer Node, specifically configured to enable data transfers across wide area networks. These goals are being realized through network optimization with tools such as perfSONAR for monitoring network performance. The project accelerates the adoption of national best practices in big data movement and contributes to South Dakota's continued competitiveness as an EPSCoR state.