CC-NIE Integration: ANSE (Advanced Network Services for Experiments)

近年来，研究和教育网络领域发生了巨大的变化，为用户社区提供了更快的数据速率和新颖的服务。ANSE项目将这些新服务，特别是动态带宽分配（DYNES）和实时网络性能监测（perfSONAR），与大型强子对撞机（LHC）部署的更高级别的工作负载管理软件（如ATLAS和CMS实验中的PanDA和PhEDEx）集成在一起。软件定义的网络方法，特别是ESnet和Internet2网络中已经提供的动态带宽分配能力的使用，使网络成为真正可管理的资源，与计算（CPU）和存储（磁盘和磁带）资源相当。这种方法通过数据和工作负载管理系统中的协同调度和截止日期调度算法实现了资源利用的智能优化。ANSE系统对实时情况做出反应，如网络事件或节点故障，以及使用特定参数，如数据分发优先级的变化。重视网络的多领域方面，这是全球分布式系统所需要的。虽然ANSE的主要目标是大型强子对撞机实验，但ANSE项目显然也适用于其他数据密集型科学领域。通过使用公认的标准，如OGF NSI， ANSE适用于大型强子对撞机的用例之外，特别是全球范围内的所有分布式计算和数据密集型应用。

Recent years have seen a dramatic evolution in the area of research and education networking, enabling faster data rates and novel services to user communities. The ANSE project integrates these new services, in particular dynamic bandwidth allocation (DYNES) and real-time network performance monitoring (perfSONAR), with the higher level workload management software deployed for the Large Hadron Collider (LHC), like PanDA and PhEDEx in the ATLAS and CMS experiments, respectively. A software defined networking approach, and in particular the use of dynamic bandwidth allocation capability, already available in ESnet and Internet2 networks, makes the network a truly manageable resource, on par with computing (CPU) and storage (disk and tape) resources. This approach enables smart optimization of resource utilization through co-scheduling and deadline scheduling algorithms within data and workload management systems. The ANSE system reacts to real-time conditions such as network events or node failure, as well as usage specific parameters like changes in data distribution priorities. Importance is given to the multi-domain aspects of networking, as required for a globally distributed system.While the primary target for ANSE is the LHC experiments, the ANSE project is clearly applicable to other data intensive science fields as well. Through the use of recognized standards such as the OGF NSI, ANSE is applicable beyond the use case of the LHC, and in particular to all distributed computing and data intensive applications of global scale.