Software-Defined Network for End-to-end Networked Science at the Exascale
Software-Defined Network for End-to-end Networked Science at the Exascale
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用于百亿亿级端到端网络科学的软件定义网络
DOI：
10.1016/j.future.2020.04.018
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Yang, Xi
中科院分区：
文献类型：
--
作者：
Monga, Inder;Guok, Chin;MacAuley, John;Sim, Alex;Newman, Harvey;Balcas, Justas;DeMar, Phil;Winkler, Linda;Lehman, Tom;Yang, Xi
领域科学应用程序和工作流程目前被迫将网络视为一个不透明的基础设施，他们将数据注入其中，并希望它以可接受的体验质量出现在目的地。应用程序几乎没有能力与网络交互以交换信息、协商性能参数、发现预期的性能度量或真实的实时接收状态/故障排除信息。这里介绍的工作是由一个新的智能网络和智能应用程序生态系统的愿景，将提供一个更确定性和交互式的环境领域科学工作流的动机。用于Exascale端到端网络化科学的软件定义网络（SENSE）系统包括基于模型的架构、实现和部署，其能够跨管理域实现自动化端到端网络服务实例化。基于意图的接口允许应用程序表达其高级服务需求，智能协调器和资源控制系统允许基于单个应用程序和基础设施运营商要求定制可扩展性和实时响应性。这允许科学应用程序将网络作为一流的可扩展资源进行管理，这是仪器，计算和存储系统的当前实践。在生产网络和测试平台上的部署和实验验证了SENSE的功能和性能。基于仿真的测试验证了支持研究和教育基础设施所需的可扩展性。这项工作的主要贡献包括架构定义、参考实现和部署。这为智能网络服务的进一步创新提供了基础，以加速大数据、云计算、机器学习和人工智能时代的科学发现。
Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.
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