Our lab, as a whole, works with tons of data; petabytes and petabytes of data. But the size of the clusters we're creating using CCP is pretty robust. The largest experiment I've run on CCP had about 1.6 terabytes of intermediate, workflow data. Our lab works with a lot more. Given more resources, we could definitely scale up to much larger data sets.

Since we're using on-prem, there are obviously limits to the amount of hardware that's on that on-prem resource store. We haven't needed to exceed that. We have other platforms that we can run larger datasets on, but as far as scalability goes, we haven't tried to scale up too far.

In the near future our plans to increase usage are not in the size of a single cluster. We're doing a demo with Cisco at Internet2's TechEx conference in New Orleans this year. We're going to train 15 to 20 network engineers how to use CCP and how to deploy clusters. We're going to be using a single CCP environment to deploy 15 to 20 clusters at the same time. As far as compute goes, our current resource pool will be able to handle that. The main thing that is going to be a stress test for CCP is having 15 clusters all trying to deploy at once and getting all the networking stuff configured. That will be a really cool test for CCP.

I think it should handle it. All the Cisco points of contact have told me it's good to go. To my knowledge, it should be fine, as long as there are enough allocatable IP addresses. That's one of the main limits: making sure you have enough IP addresses for all those clusters.