I'm hoping here that this post isn't out of line with the scope of the NANOG list, of which I've been a long time lurker. If so, please just ignore me. We're trying to calculate Jitter of a variable (non-limited) size data set. One Jitter formula that we see cited occasionally (and is in RFC 1889 - I believe iPerf uses this formula for it's Jitter #'s) looks something like this: J = J+(|D(i-1,i)|-J)/16 The problem with this formula is that it works best on small sample sets, and it also favors more recent samples. As the sample size grows, the jitter of early samples seem to get factored down to basic "noise", and then aren't really well represented in the overall Jitter number. We're trying to find a viable formula for showing a general Jitter "average" over a period of time. One possibility here is just to iterate all samples like this: Jsum = Jsum+|D(i-1,i)| and then calculating the jitter like this: J = Jsum / (sample count - 1) The sample count could be anywhere from 2 to 1 million (or more). This formula does seem to represent early sample in the "Jitter" number just as strongly as later samples, but seems like it might be a bit simplistic. Does anyone have any feedback on this alternate way of calculating Jitter, or any better ways to do this? Thanks in advance for any input. Jeff Murri Nessoft, LLC jeff@nessoft.com www.nessoft.com