The volume of data moving through a network increases with new scientific experiments and simulations. Network bandwidth requirements also increase proportionally to deliver data within a certain time frame. We observe that a significant portion of the popular dataset is transferred multiple times to different users as well as to the same user for various reasons. In-network data caching for the shared data has shown to reduce the redundant data transfers and consequently save network traffic volume. In addition, overall application performance is expected to improve with in-network caching because access to the locally cached data results in lower latency. This paper shows how much data was shared over the study period, how much network traffic volume was consequently saved, and how much the temporary in-network caching increased the scientific application performance. It also analyzes data access patterns in applications and the impacts of caching nodes on the regional data repository. From the results, we observed that the network bandwidth demand was reduced by nearly a factor of 3 over the study period.

中文翻译：

---

分析科学数据共享模式以进行网络内数据缓存

通过新的科学实验和模拟，通过网络传输的数据量不断增加。网络带宽要求也成比例增加，以便在特定时间范围内传送数据。我们观察到，由于各种原因，流行数据集的重要部分多次转移到了不同​​用户以及同一用户。对于共享数据的网络内数据缓存已显示出减少了冗余数据传输并因此节省了网络流量。此外，由于对本地缓存的数据的访问会导致较低的延迟，因此预期通过网络内缓存可以提高整个应用程序的性能。本文显示了在研究期间共享了多少数据，因此节省了多少网络流量，以及临时的网络内缓存在多大程度上提高了科学应用程序的性能。它还分析了应用程序中的数据访问模式以及缓存节点对区域数据存储库的影响。从结果中，我们观察到在研究期间，网络带宽需求减少了将近3倍。