I was using linear maths, but the case is even worse if you do proper statistical anaylis. In that case, their math must be something like: We can accomodate 75% of subscribers at 80% at peak usage times.
The problem is that a cap does not make people use the network efficiently - access to the network will form a gaussian distribution, and subscribers will perceive service as bad when peak cases are not handled to a degree. This is the reason why voice operators (cellphone or landline) do two things:
1) Allocate free minutes to users evenly through the course of the month. This is to prevent everybody calling on the same day (when they receive their minutes)
2) Cellphone packages are cleverly designed to balance load on the network - (i.e. not only "use the network on weekend", but try to use the network for that much over weekends and that much during weekdays)
So my response is: yes, i've not done proper statistical analysis, and my reply is, lets do proper statisical analysis. DFantom, the statics you mentioned, is in fact only linear math - multiplying percentages.
As to the suggestion that transparent proxies etc. will really help I can't see how it will:
1) Proxies does have a limited effect on peer-to-peer
2) Proxies are useless for streaming media
3) Proxies are useless for online gaming
4) Proxies are useless for e-mail
5) Proxies are (almost) useless for websites that generates content dynamically
You do not use 3GB by surfing the web (large http/ftp transfers excluded, and I'm not certain how much of that is cached)
Of course local access does make a difference, but if international bandwidth is not that important, can somebody please explain to me why ADSL users are kicking and screaming when they reach their cap?
MWeb can get away with higher contention ratios because
1) Users pay for the time they spend online, their usage time is shorter
2) Because of callmore, their users are more evenly distributed in time
3) Dialup users use the internet more for e-mail, html etc. of which html is cached, instead of long running peer-to-peer, streaming media and large downloads.
4) They already have contention ratio's on the number of incoming lines
Broadband users have a 24/7 connection, and thus are using it when it is most convenient for them. The problem is that you get a lot of peak demand times with this scenario.
If we would like to do this proper statistical analysis we need to have more information:
1) What is the average amount of data that users transfer a day
2) What is the standard deviation of this mean
3) What is the usage patterns time-wise (hour-to-hour)
4) What is the usage patten during the month (do you go for your cap in the first day, the last day or spread it evenly)
5) What is the ratio between local/international access
6) what is the cacheable transfers
I would also like to note that some broadband users expect a leased line - which, for obvious reasons is ridiculous.