I think this is a nice take on using clouds (really I think your looking hard at CDNs here) for font end demand. Certainly the MJ incident yesterday is a great example of peaking and some sites without reliable fail over CDN partners slowed or went down.

Where the simplicity of the fail over model will really be, and is already being, tested is the real time web. Many of the site examples you give here the demand is mostly on read operations for content, not writes. Twitter would no doubt love to fail over to a CDN, but CDN’s don’t do well with write operations, and can’t due to data-coherency issues on the back end.

Net, net, the nice segmentation and fail over you speak of here is fine for most applications, but real time aps face a bigger challenges that may require a full fledged cloud development model.

Thanks for being really funny all day yesterday!

James @wattersjames

However, based on your observations, do you now see an opportunity to deliver distinctive new pre-sales consulting services to enterprise CIOs?

As an example, could a “predictive analytics” modeling tool aid the CIO decision making process of on-demand cloud service procurement. Also, we’ve used Erlang formulas in the networking arena to determine load balancing, do you see the evolution of similar formulas in the emerging cloud services space?

Moreover, is there demand for a dashboard-like application that would enable a CIO to use historical data from a hybrid solution to predict upcoming peaks — and then purchase “just in time” capacity from pre-approved Service Providers that offers the best deal?

The whole “supply vs. demand” — and augmentation balancing — topic seems to be a place where meaningful differentiation can emerge in the marketplace.

David Deans
Business Technology Roundtable