Asset Details
Do you wish to reserve the book?
Prospects for Wideband VLBI Correlation in the Cloud
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Prospects for Wideband VLBI Correlation in the Cloud
Do you wish to request the book?
Prospects for Wideband VLBI Correlation in the Cloud
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Prospects for Wideband VLBI Correlation in the Cloud
2019
Overview
This paper proposes a cloud architecture for the correlation of wide bandwidth Very Long Baseline Interferometry (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. Here, we describe benchmarks of VLBI correlation using the DiFX-2.5.2 software on the Google Cloud Platform to assess cloud-based correlation performance. In our analysis, the number of virtual central processing units 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. Based on the results, we 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. We note that 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.
Publisher
IOP Publishing Limited
