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The Changing Face of Concurrency


Service providers operate their networks based on a series of calculations. Among the variables they consider is concurrency, or the number of subscribers likely to be tuned in or logged on at any given time. In recent modeling, cable operators have planned for roughly 10% concurrency with video and 1% concurrency with high-speed data. (The 1% number may be higher now. I have that figure from 2006.) If there are a thousand subscribers, that means 100 could access video streams at the same time via cable TV and 10 could use the promised 6 Mbps (or 8 Mbps, or 10 Mbps…) for Internet surfing all at the same time. (Or 20 subscribers could use 3 Mbps. Or 60 could use 1 Mbps. You get the idea.)

I bring this up because Ars Technica has an article posted about the FCC hearing on Comcast’s broadband traffic management policies. Forget the controversy itself for a moment. The article includes a paragraph talking about how Comcast cannot fully support its product because its network is not built to handle peak usage all the time. The last sentence acknowledges that Comcast defends itself by saying no networks are built that way. But the acknowledgement is a throw-away line at best, as if Comcast had just created the statement to argue its point further.

Concurrency modeling is most certainly not something Comcast made up. In fact, if I understand things correctly, our power grids operate the same way, which is why on hot days there are brownouts and blackouts in some regions as everyone turns the air conditioner on high. Until now, the 10% and 1% concurrency models have worked quite well in the cable industry. They make economic sense and they’ve usually met subscriber bandwidth needs. Comcast shouldn’t be blamed for operating on an industry standard. The problem is, that standard has to change.

On the data side, multimedia applications (P2P and otherwise) are raising the bandwidth rates consumers use for common tasks, making it much more likely that they’ll hit peak usage in greater numbers. On the video side, on-demand video is raising concurrency rates because suddenly subscribers have a lot more video streams to choose from. Chances are there’s always something on that a subscriber would want to watch. In conservative estimates, on-demand services will raise concurrency rates from 10% to 25% by 2012.

We’ve used the term “always on” since broadband first became popular. Maybe we should change that now to “always on high.”

7 Responses

  1. I wish it was known what Cox Communications concurrency numbers are at present. The package I subscrive to is for 15Mbps down, when I am luck to get 2Mbps. So my guess is that the node I am on has been over extended.

  2. […] 57x over ten years. Above is a graph showing Nielsen’s prediction alongside actual data points of bandwidth availability. He is remarkably on […]

  3. […] The virtual riot the BBC started when it launched the iPlayer last year may pale in comparison to what happens when the programmer starts streaming BBC1 live in the next few months. That’s right – the BBC is going to offer its broadcast live on the Internet. Now instead of facing increased bandwidth demand spread out over a period of time (iPlayer currently streams select BBC programming after it has aired), ISPs could be faced with massive concurrent demand. […]

  4. […] data point: time spent watching time-shifted TV increased 56% year over year. That changes a lot of business and technical models that have been in place for […]

  5. […] created a bandwidth calculator tool several years ago that I’ve referenced a couple of times in the past. However, despite my own excitement over the tool, it’s never gotten […]

  6. […] but with more variables thrown into the mix. The model now includes considerations for multiple concurrency levels for different services (IP video versus traditional VOD, for example), whether a service is early […]

  7. […] demand is assumed to grow both because of higher-bandwidth applications and because of higher concurrency levels. To counter that, it’s assumed operators will continue to decrease node sizes in the coming […]

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