(Original at DSL Reports)
. . . And Caps Don’t Really Address Truly Disruptive Users
“The correlation between real-time bandwidth usage and data downloaded over time is weak and the net cast by data caps captures users that cannot possibly be responsible for congestion.”
You might recall that back in 2009, we mentioned a piece claiming that the “bandwidth hog,” a term used ceaselessly by industry executives to justify rate hikes, net neutrality infractions, and pretty much everything else — was a myth. The piece was penned by Yankee analyst Benoit Felten and Herman Wagter, who knows a little something about consumption — as he’s the man largely responsible for Amsterdam’s FTTH efforts. The problem wasn’t bandwidth hogs, argued Wagter, the problem was poorly designed networks built by people more interested in cutting corners than offering quality product.
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In a blog post, Felten notes that the pair took real user data for all customers connected to a single aggregation link and analyzed the network statistics on data consumption — in five minute time increments — over a whole day. What they found is that capping ISPs often don’t really understand customer usage patterns, and are confusing data consumption (how much data was downloaded over a whole period) and bandwidth usage (how much bandwidth capacity was used at any given point in time).
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To simplify, one of our readers puts the dreaded highway metaphor, often used by ISPs to justify caps, to work in the opposite direction:
1% of vehicle drivers on the road travel a disproportionate amount of miles compared to the average driver. But they are on the road all the time. Most of the time they are on the road there is no rush hour congestion.The heavy drivers are likely to be involved in rush hour traffic jams, but only represent a small, not terribly relevant, fraction of total drivers in the traffic jam.Limiting the amount of miles a driver can drive, does nothing to widen the roads and little to keep people off the roads during traffic jams, thus does not help with congestion.
The researchers themselves note that blunt caps simply may not work, and they punish those that aren’t really causing any network problems:
Assuming that if disruptive users exist (which, as mentioned above we could not prove) they would be amongst those that populate the top 1% of bandwidth users during peak periods. To test this theory, we crossed that population with users that are over cap (simulating AT&T’s established data caps) and found out that only 78% of customers over cap are amongst the top 1%, which means that one fifth of customers being punished by the data cap policy cannot possibly be considered to be disruptive (even assuming that the remaining four fifths are).
Data caps, therefore, are a very crude and unfair tool when it comes to targeting potentially disruptive users. The correlation between real-time bandwidth usage and data downloaded over time is weak and the net cast by data caps captures users that cannot possibly be responsible for congestion.
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