论文标题
Exascale的端到端网络科学的软件定义网络
Software-Defined Network for End-to-end Networked Science at the Exascale
论文作者
论文摘要
当前,域科学应用程序和工作流程流程被迫将网络视为不透明的基础架构,他们注入数据并希望它以可接受的经验质量出现在目的地。应用程序与网络交互的能力几乎没有能力交换信息,协商性能参数,发现预期的性能指标或实时接收状态/故障排除信息。此处介绍的工作是由新的智能网络和智能应用程序生态系统的愿景的动机,该系统将为域科学工作流提供更确定性和更具互动性的环境。 Exascale系统端到端网络科学的软件定义网络(Sense)系统包括基于模型的体系结构,实现和部署,该网络可以跨管理域启用自动化的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要求,智能的编排和资源控制系统允许根据个人应用程序和基础架构操作员的要求自定义定制可扩展性和实时响应能力。这使科学应用程序可以作为一流的可计划资源来管理网络,以及当前的仪器,计算和存储系统的实践。生产网络和测试床上的部署和实验已验证了感觉功能和性能。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。这为在大数据,云计算,机器学习和人工智能时代的时代进一步创新了智能网络服务的进一步创新。
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.