Currently, most enterprise grid implementations reside in large enterprises like Hartford Life, Bank of America, and Royal Dutch Shell. The typical model is that of taking a complex computing problem, dicing it up into smaller problems, and distributing it to loosely stitched networks of grid-enabled computers.
Implementations of this type are what are termed ”compute grids”, or linked servers and desktops that create what is essentially a virtual supercomputer.
Clearly, this model doesn’t lend itself well to broad adoption. The applications running on compute grids are often developed in house, and the grids themselves tend to be application specific and resource heavy.
According to Steve Tuecke, CEO of Univa, a grid software startup, organizations accomplish one thing when using compute grids: they take a single application and enable it to run faster by sharing computations across distributed resources.
”The problem is that you end up creating grid silos,” Tuecke said, ”so you end up with an application that can run in parallel across different resources, but you still have a silo.”
Two other types of grids, data grids and resource grids, promise more flexibility.
Data grids distribute database information and storage, while resource grids enable the broad sharing of infrastructure, including servers, storage, and other data-center resources.
In order to stitch all of these disparate resources together, early adopters and grid startups are setting their sights on open standards.