“Data caps are controversial,” said consultant Martin Geddes, without a trace of irony. “Many people think they are a scandalous price-gouging tool for greedy ISPs.”
“Others think they are a reasonable response to the gluttony of users,” Geddes says. The reality is more complex. For starters, two problems really must be addressed, he says. Rhetoric about the “end of scarcity” aside, network capacity actually is finite, while demand use of the networks theoretically is nearly infinite.
And a thoughtful person will possibly agree that usage caps actually do not solve the congestion problem at all. There are other real economic problems caps might be said to solve, but congestion probably is not one of them.
Nevertheless, congestion is a real problem for IP networks, in large part because of the way the protocol works. In Martin’s newsletter, you can read the full post where he talks about this issue.
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Geddes argues that one of the problems is scheduling. The reason is that there are several distinct types of customers on any network. Some are demanding customers important for all sorts of reasons to an Internet service provider, and sometimes that importance relates to the economic value of those customers (high value business customers who pay quite a lot, or users who run important mission critical services).
In other cases, there are lots of customers who would gladly choose a variable quality service with truly unlimited access, and would rather pay less money for their service, and can tolerate quite a lot of variable quality.
Most customers are probably somewhere in the middle. They’d rather pay a reasonable amount of money and experience a reasonable amount of quality.
Geddes says overall usage of the network resource is an issue, since an IP network is, by definition, a shared protocol with contention protocols that actually become unwieldy precisely when contention is the highest.
The solution, he argues, will ultimately have to involve some method of managing the “scheduling” of packet delivery, not necessarily the amount of data consumed overall. And that means pricing by “quality,” not “consumption,” as such.
The analogy is pizza delivery. Prices should be set by how hot the pizza is guaranteed to be when delivered, not how many pizzas are delivered, or even necessarily when those pizzas are delivered. The highest prices will correlate with the hottest pizzas, no matter when delivered. The lowest prices will correlate with the pies that might have varying temperatures, but might sometimes even be cold. The mid-point of pricing will be for delivery that might feature hot pizzas, but will probably mostly feature warm pizzas, never cold pizzas.
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Edited by Brooke Neuman