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Distributed Computing Economics
machaut writes "In a ClusterComputing.org article, Jim Gray, director of Microsoft's Bay Area Research Lab, provides an interesting economic analysis for building distributed systems. When do you choose a grid over a cluster or a supercomputer?
When does it pay off to move a task to the data vs moving the data to the task? He takes current hardware and networking costs into account to answer those questions."
.. have already figured it out - let other willing users pay the power bill, bandwidth cost, etc. and crunch the data in their spare time. Seems to be working well for seti@home, etc.
Of course, if you are working with sensitive data (military stuff, major trade secrets, etc.) your security/privacy needs will outweigh the costs involved with doing it all in house.
Don't blame me, I voted for Kodos
All the work must(should) be double checked to make sure everything is correct
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Just last year we were discussing data transfer over the time it would take to overnight some data in a package, worked out that it was faster and wouldn't clog up our line to burn the DVDs and send them through an international package service vs send it over the T1s. I think with all but the largest businesses this is probably still true for larger (Gigabytes) amounts of data. Network costs are too high to be putting data far from where it is to be used. Whether CEOs realize it or not, this has a great effect on the ways businesses with multiple locations structure their company and work together.
It's a nice piece of analysis. Someone could have done it 8 years ago when Java came out; the facts are not significantly different (The values are different of course but the ratios involved are pretty similar. I did some thinking along these lines back then, and then in 2000 when considering working for a "hot P2P company" that an old acquaintance of mine was running.)
My thinking went something like this: There are only a few, "niche" applications which need more compute power and which people pay for (distributed rendering, CFD, FEA, maybe a couple others). Maybe you could build that into a 10-30 million dollar business if you overcame a zillion obstacles but it didn't look like a billion or multi-billion dollar business. The applications for which people buy beefy servers, and which have a monetary payback, are mostly database applications. For those, you need to move the entire database near to the number-crunching PC, and that's not really feasible due to the cost of transporting Gigabytes of data or the unlikelihood that the PC's hard disk can store all the giga/terabytes of information potentially relevant for the computation. Not to mention the security problem.
And Jim Gray's analysis lays out in more precise economic terms why it doesn't make sense. I like how he even calculated the relative merits of a Beowulf-like cluster of PCs versus P2P which I never really analyzed (I lumped them together as basically similar.)
That said, has anybody even made a stab at designing or implementing a relational database with a P2P architecture? I know that there's Oracle Cluster Server, but I'm thinking of something more low-end and more distributed.
--LP
The whole goal of distributed computing is to externalize costs. When someone else bears your cost...then, yeah, it's free. This is the idea behind P2P. P2P is significantly less efficient than specialized servers, but externalizes costs. In some cases, SETI type arrangements use real idle equipment. In other cases, it pushes real costs onto other unwitting groups.
Your employer has to pay for the electricity if you leave a bunch of computers on at night to help calculate protein folds. It is not necessarily a bad thing...just that an unwitting party is bearing a cost. In many cases the cost is of little consequence...in some cases it is.