For a loadtest I have to figure meaningful numbers for the maximum number of requests per hour and minute. The only thing I have is the number of requests per working day (which is a timeframe of 14 hours).
The easiest (but most inaccurate) would be just to take the average but I think it follows a normal distribution. Here is what I have:
There are people generating requests over the day from 06:00 (am) - 20:00 (pm) - afterwards the system is taken offline. They a generating an overall load of 1400 requests each day. So my question is: How could those requests be distributed over the day? I have something like this in mind (this is just an (unrelated) example but it should give an indication what I would image):
I think the red curve comes the closest to my scenario since few people start at 6:00 or work until 20:00. The workload will increase at 9:00 and flat out at 17:00 => So like a normal Gaussian distribution...
How can that scenario be mapped into real data? I am especially interested in the peak number of requests during the day. So what could be a potential maximum number of requests per hour/minute? I know (as indicated by several graphs) that there are several potential distributions. (But the sum should always be 1400 in the end)
I currently use excel to visualize the numbers/diagrams.
- Please note that the image is just an example which I highjacked to illustrate what I have in mind
- I do not have the actual data - only the daily number of 1400. If I had the actual timestamp of each request I wouldn't need to ask this question to figure out a realistic distribution scenario.
- I can not measure since it is historical data and this is also not an exam question ;-)
=> So the overall question is: What could be a possible distribution scenario to spread the requests over the day? I assumed that they followed a normal distribution and asked how that could look like...