云中宽带VLBI关联的前景
Prospects for Wideband VLBI Correlation in the Cloud
摘要：
本文提出了一种宽带VLBI数据相关的云架构。云相关性有助于使用灵活分配且几乎无限的计算资源并行处理整个实验。这种方法提供了一个潜在的改进，专用的相关集群，这是由一个固定数量的安装处理器节点和回放单元的约束。此外，云存储提供了一种替代方案，可以维护可能间歇性使用的硬盘驱动器。我们描述了使用DiFX-2.5.2软件在Google Cloud Platform上进行VLBI相关的基准测试，以评估基于云的相关性能。每个虚拟机的虚拟CPU数量是不同的，以确定云资源的最佳配置。观测站的数量是变化的，以确定与不同大小的VLBI阵列的相关时间的比例。还测量了从Google Cloud Storage到执行相关性的虚拟机的数据传输速率。我们还提供了一个示例云相关配置。当前的云服务和设备定价数据用于编制成本估计，从而可以在云和集群处理之间进行近似的经济比较。经济比较是基于成本数字，这是一个移动的目标，并高度依赖于因素，如利用集群和媒体，这是一个挑战，以估计。我们的模型表明，转移到云是一个高数据速率，低占空比宽带VLBI相关的替代路径，应继续探索。在VLBI相关的生产阶段，云有可能大大减少数据处理时间，并允许在给定的一年中处理更多的科学实验，用于天文学和大地测量VLBI应用中越来越常见的拍字节级数据集。
英文摘要：
This paper proposes a cloud architecture for the correlation of wide bandwidth VLBI data. Cloud correlation facilitates processing of entire experiments in parallel using flexibly allocated and practically unlimited compute resources. This approach offers a potential improvement over dedicated correlation clusters, which are constrained by a fixed number of installed processor nodes and playback units. Additionally, cloud storage offers an alternative to maintaining a fleet of hard-disk drives that might be utilized intermittently. We describe benchmarks of VLBI correlation using the DiFX-2.5.2 software on the Google Cloud Platform to assess cloud-based correlation performance. The number of virtual CPUs per Virtual Machine was varied to determine the optimum configuration of cloud resources. The number of stations was varied to determine the scaling of correlation time with VLBI arrays of different sizes. Data transfer rates from Google Cloud Storage to the Virtual Machines performing the correlation were also measured. We also present an example cloud correlation configuration. Current cloud service and equipment pricing data is used to compile cost estimates allowing an approximate economic comparison between cloud and cluster processing. The economic comparisons are based on cost figures which are a moving target, and are highly dependent on factors such as the utilization of cluster and media, which are a challenge to estimate. Our model suggests that shifting to the cloud is an alternative path for high data rate, low duty cycle wideband VLBI correlation that should continue to be explored. In the production phase of VLBI correlation, the cloud has the potential to significantly reduce data processing times and allow the processing of more science experiments in a given year for the petabyte-scale data sets increasingly common in both astronomy and geodesy VLBI applications.
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