Just got back from Chicago, where over the past 2 days a small group of scientists, academia and industry discussed various aspects of cloud computing and related topics. 

One of the topics was about comparing extreme large scale analytical problems and the systems leverage to solve them. In order to compare classes of super computers, Alex Szalay (John Hopkins University) explained a simple yet interesting figure: The AMDAHL number (Amdahl's Law Bell, Gray and Szalay 2006)

Alex explained the Amdahl number (BW) as One bit of IO/sec per instruction/sec.

Why is this figure interesting?  To compare the analytical capabilities of various extreme large clusters, it is important to categorize them into different groups based on their processing capabilities. Typical commercial applications of large scale analytics require large amounts of IO per available CPU while various scientific applications require less IO per available CPU.

For a Blue Gene the BW=0.013, the JHU cluster BW = 0.664. So off

Over the last year I have spent a good amount of time thinking about the cost of analytics, and a few things worry me about our industry and how vendors price in this industry.  In the Data Warehousing and BI industry, we're starting to see pricing models based on data volume or size. I know of one vendor which prices by specint, so - get a bigger system or virtualize your systems - get a big bill.

 The problem with these licensing schemes is that they actually make the cost of doing analytics more expensive over time.  If I am a good technology user, over time I'm driving down the costs per unit of data volume to create and maintain the data flowing into my DW.  In the simple case you could say that the cost to generate data is declining at the inverse rate of Moore's law, particularly for a web business where most of the cost is in hardware.

Back to the point, using Moores Law as the example, the cost of data generation declines per unit of volume at a rate of ~50% every 18 months.