As described in the prior section, a grid is essentially a set of computing resources shared over a network. Grids differ from more traditional distributed systems, such as the classic n-tier systems, in the way its resources are utilized. In a conventional environment, resources are dedicated: a PC or laptop has an owner, and a server supports a specific application. A grid becomes useful and meaningful when it both encompasses a large set of resources and serves a sizable community.

The large set of resources associated with a grid makes it attractive to users in spite of the overhead (and the complexity) of sharing the resource, and the grid infrastructure allows the investment to be shared over a large community. If the grid were an exclusive resource, it would have to be a lot smaller for the same level of investment.

In a grid environment, the binding between an application and the host on which it runs begins to blur: the execution of a long-running program can be allocated to multiple machines to reduce the time (also known as wall clock time or actual time) that it takes to run the application. Generally, a program designed to run in parallel will take less time to run as more nodes are added, until algorithmic or physical bottlenecks develop or the account limits are reached.

Two assumptions must hold for an application to take advantage of a grid:

• Applications need to be re-engineered to scale up and down in this environment.
  
  
• The system must support the dynamic resource allocation as called by applications.

As technology advances, it will become easier to attain both these conditions, although most commercial applications today cannot satisfy either of them without extensive retrofitting.