Grid Computing: Ahhh, But We Do Still Need Servers

Nicholas Carr wrote a post that argues that with the introduction of grid and utility computing, the server market will decline. In the post Mr. Carr states,

In this scenario, the core unit of business computing would not be small, inflexible servers but rather large, flexible computing clusters or grids. These clusters in turn would be built not from traditional branded servers but from cheap, commodity subcomponents - chips, boards, drives, power supplies, and so on - that the grid operators would assemble into tightly networked physical or virtual machines. Many of the functions and features built into today's branded servers would be taken over by the software running the cluster.

Dan Ciruli responds by pointing out,

Computes just aren't the same. Computes look different on different operating systems. Not all software runs on all operating systems. Different people prefer different toolsets, and they always will. Some OSs are better for some things than others, and people choose the appropriate OSs for them. Yes, we've all read about "write once, run everywhere" software--but a small minority of software actually runs that way. OSs are different, and they will continue to be different. People will continue to write software that takes advantage of particular OSs.

But I don’t think Dan went far enough. I agree with Dan and I wanted to add a few points as to why.

Do you remember these famous quotes?

”I think there is a world market for maybe five computers.” – Thomas Watson, IBM, 1943

”There is no reason for anyone would want a computer in their home.” – Ken Olson, Digital Equipment Corp, 1977

“640k ought to be enough for anybody.” - Bill Gates, Microsoft, 1981

Our human nature forces us to push new technology. To see what new things we can build with it and do with it. (We are curious creatures.) Grid computing allows a company to get more work out of its servers and desktops. If you think of grid computing using current technology paradigms, I agree that it looks like there will be a decline in the server market. But history has proven that when people are given more computing power they find new things to do. What we are really going to see with the increased acceptance of grid computing is an increased need for computing power. Just image the new technologies and applications we will invent with the increased availability of affordable super computing power. Look at what Google can do with their “super computer.” And don’t think for a minute that companies don’t want the opportunities currently available to Google. When we, as the technology sector, accept the grid paradigm shift we will see a surge in the server market, not a decline.

In addition there will continue to be a need for multiprocessor boxes because some computations are better suited to a threading model than a grid model. Grid and threading technologies are complimentary. The Digipede grid computing model offers a methodology for simplifying some threading cases, but if your application uses micro-threads in a thread pool you will still want a multiprocessor box. And as Dan pointed out there are other aspects to the grid solution; like where your data lives and what OSs and applications your team is already trained in.

Technology acceptance is organic and I don’t think Geoffrey A. Moore was that far off when he wrote about the “Technology Adoption Life Cycle.” We build on what we know and on what makes us feel safe. That’s how we’re built. There may be a time when servers will become completely commoditized but it won’t be grid computing that makes that happen because the process has already started and grid computing isn’t widely accepted. The only question concerning servers is which company will be able to make that shift from high-end servers to commoditized servers.

Server commoditization coupled with grid computing makes the next 10 years in tech look as exciting as the PC revolution. It’s a good time to be an engineer.

Technorati tags: Digipede, Grid Computing, Distributed Computing, Utility Computing, Multithreading, Thread Pool, Google