I had a longer response, but my f'n (fine) ISP dropped my adsl connection 
and my hotmail connection timed out grrrrrrrrr.

Formula here:
http://mathworld.wolfram.com/PoissonTheorem.html

In your example:
p = 0.0003 (100000 per month downto interval of ms)
n = 1 (length of request time)
k = 2 (what to check probability of)

Note: you are interested in k>=2 so you will have to calulate 3,4, etc.

Also note that your demand isn't going to follow a random distribution. But 
your history logs will. So it makes more sense to divide your analysis into 
smaller chunks. ie 4 hr blocks, and use traffic history / projections to 
calculate p for that given time block.

Also it may be more useful to project the number of times that the tolerance 
specified was exceeded.

Probability of 2 requests initiating in the same ms = 0.0000000450

I will do as an XLS using request time, traffic in block time and interval 
in blocktime to build a probaility table.

If interested.

HTH
Eric


Lets say you have 1 page that scores 1,000,000 unique visitors a month

here's what I came up with:

1,000,000/30/24/60/60/1000 = 0.000385

So that means that 0.0003 people a milisecond hit the sight, correct?
what is the % chance of 2 people hitting on the same milisecond (and math
equation for that?)
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