It's Sun's thesis about the explosive growth in demand for raw computing power--but is it more than the utility computing model warmed over?
For nearly a decade as chief technology officer at Sun Microsystems, Greg Papadopoulos has mulled the best way to build and sell high-performance computers, and more recently how to pull the company out of its financial slump. Earlier this year, after multiple conversations with Sun customers and other CIOs, Papadopoulos says he had a lightning-bolt insight--that an elite group of companies are consuming inordinate amounts of IT infrastructure, well beyond most other businesses, and that their demand is growing exponentially. This trend, Papadopoulos maintains, has implications not just for IT's most insatiable consumers, but for the structure of the computing industry itself--and, naturally, for Sun.
Papadopoulos calls this theory the "red shift." In astronomy, the term refers to what happens to the wavelength of light emitted from an object traveling away from the observer: It lengthens, moving to the redder end of the visible spectrum. Predicted by Christian Andreas Doppler, who first observed the phenomenon in sound waves, the optical red shift was used by Edwin Hubble to demonstrate that the universe is expanding.
Red-shifting companies will experience explosive growth, predicts Sun's Papadopoulos
Photo by Gabriela Hasbun
In IT, Papadopoulos uses red shift to describe the rapidly expanding universe of computing demand as data processing requirements--not only from Web companies like Google, YouTube, MySpace, and Salesforce.com, but also from large conventional users of high-performance computing like pharmaceutical, financial, and energy companies--exceed the ability of Moore's Law to keep up.
It's not just about how many CPU cycles a company uses. Papadopoulos argues that red-shift companies will enjoy exponential business growth in the coming years. Blue-shift companies--those whose processing needs aren't exploding--will grow at about the same rate as GDP, he says.
There's an apparent contradiction in this theory. It will be hard for red-shifting companies to grow at exponential rates if they must spend massively to expand their infrastructures. Papadopoulos' answer to that quandary is, naturally, Sun-centric: A shift to lower-cost, lower-risk utility computing, mostly on sophisticated "big iron" servers, will allow these businesses to overcome the inherent limitations of Moore's Law, he maintains.
To be sure, the red-shift theory is part techno-economic insight and part hype. In talks on the subject by Papadopoulos, a former MIT professor, you can detect a hint of the happy pedagogue expounding on his pet thesis. But there's hard evidence as well: mostly in the growth of cloud-computing platform providers, from giants such as Amazon.com, whose on-demand storage service, called Amazon S3, contains 5 billion objects, up from zero less than a year ago at launch, to lesser-known players like 3tera, which is seeing 100% quarterly growth for its utility computing service. The business of selling software as a subscription service is also explosive, growing 43% annually, according to a recent report by RBC Capital Markets.
At the same time, companies faced with the rising costs of powering, cooling, and maintaining racks of servers in conventional data centers are stretching their IT resources beyond capacity--and looking for alternatives. Papadopoulos uses an energy-utility metaphor to capture this shift: "Why build your own generator in your back yard when you can plug into the energy grid?"
5 Top Federal Initiatives For 2015As InformationWeek Government readers were busy firming up their fiscal year 2015 budgets, we asked them to rate more than 30 IT initiatives in terms of importance and current leadership focus. No surprise, among more than 30 options, security is No. 1. After that, things get less predictable.
InformationWeek Tech Digest, Nov. 10, 2014Just 30% of respondents to our new survey say their companies are very or extremely effective at identifying critical data and analyzing it to make decisions, down from 42% in 2013. What gives?