Sure, I had the same problem. We had to figure out a clever way to scale this. We achieved this through a few methods.. We first started with a single drive and 5 clusters with a total of 15 servers. I created 2 total lookup files, the first one with host values to drive the first dropdown, when the user selects the app, it dynamically populates the second drop down so the user can select a single host or an aggregate of the cluster. The second lookup table holds a row for the host, slope, y intercept, and drive letter. Anytime disk is purged or added, the y intercept value will change but the slope will remain constant.
When we started to scale, we had to reduce our dependency on the lookups because it was getting difficult to maintain these values across hundreds of servers. We found a way to dynamically populate the slope value and created an additional dropdown for drives so we could do multiple drives per host.
Another approach we took to match the model name to the host value selected was to use a good naming convention for the model names. So if the user were to select a hostname in the dropdown, that hostname will be passed to the model name and will look like this | apply Forecasting_$HOST$ .
One last word of advice.. Create short feedback loops to judge accuracy. You gotta be confident in the results your getting from the forecast so creating a few panels dedicated to accuracy is important
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