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option 1 is easily corrupted, but probably not insurmountable
Option 1 would have a problem out of the box because occasionally stats data is corrupted or missing. A single corrupted sample can ruin a month's worth of reporting if it's averaged in. Missing data wouldn't bother a customer too much, but the provider would want to have established a method for billing if some data is gone. A single missed day could reduce revenues by 3% or so in the month (althought it's possible that any single missed day also reduces the true P95 billed rate). I thought about moving from peaks to averages when I was reporting aggregate network stats, but I felt it was easier to be able to throw out some samples. Any move to averages should include discards for "bad" or interpolation for "missing" samples.