IRNC: SP: International Research Instrumentation System (IRIS)

国际研究仪器系统（IRIS）是一项提供软件框架的建议，以简化端到端网络性能监测和诊断的任务，从而改善对国际科学和工程合作的支持。IRIS将提供一个全面的监测包，通过提供即时访问信息，揭示任何参与网络的状态和健康状况，这将使每个IRNC获奖者以及整个项目受益。IRIS促进了perfSONAR资源的更广泛部署，大大增加了研究人员依赖高性能国际网络连接所需的端到端路径上可用诊断资源的可能性。因此，nsf资助的科学工作，如超长基线干涉测量（eVLBI）、全球网络创新环境（GENI）和激光干涉仪引力波天文台（LIGO），更有可能通过改善其网络性能来利用可用的网络资源。通过提供具有全球适用性的关键测量工具，该建议将对广泛的国际科学和工程网络用户社区产生重大影响。IRIS将通过与IRNC项目合作，研究和实施具有挑战性的网络测量问题的解决方案，推进网络测量和监控的状态。这将对涉及全球协作和大型数据集交换的多个科学学科产生广泛影响。

The International Research Instrumentation System (IRIS) is a proposal to provide a software framework to simplify the task of end-to-end network performance monitoring and diagnostics and thereby improve support for international science and engineering collaborations. IRIS will deliver a comprehensive monitoring package that will benefit each IRNC awardee as well as the entire project as a whole by delivering instant access to information that will reveal the status and health of any participating network.IRIS facilitates a broader deployment of perfSONAR enabled resources significantly increasing the likelihood diagnostic resources will be available along the end-to-end paths needed by researchers relying on high-performance international network connections. As a result, NSF-funded science efforts, such as those within Very Long Baseline Interferometry (eVLBI), the Global Environment for Network Innovations (GENI), and the Laser Interferometer Gravitational Wave Observatory (LIGO), are more likely to be able to exploit available network resources by improving their network performance.This proposal will have significant impact on the broad international science and engineering network user community by providing key measurement tools with global applicability. IRIS will advance the state of network measurement and monitoring by working with IRNC projects to research and implement solutions to challenging network measurement problems. This to have a broad impact among multiple scientific disciplines involving global collaboration and exchange of large data sets.