Friday, December 31, 2004

How Grid Computing Differs from Cluster Computing

After doing some research on grid computing through IBM's web resources, I have come across the following outline which amplifies the differences between grid and cluster computing. This topic has been mis-understood by most people who I have discussed it with. Hopefully this will aid in my understanding and ability to discuss the topic intelligently.

Quoted from

How grid differs from cluster computing
Cluster computing can't truly be characterized as a distributed computing solution; however, it's useful to understand the relationship of grid computing to cluster computing. Often, people confuse grid computing with cluster-based computing, but there are important differences.

Grids consist of heterogeneous resources. Cluster computing is primarily concerned with computational resources; grid computing integrates storage, networking, and computation resources. Clusters usually contain a single type of processor and operating system; grids can contain machines from different vendors running various operating systems. (Grid workload-management software from IBM, Platform Computing, DataSynapse, and United Devices are able to distribute workload to a multitude of machine types and configurations.)

Grids are dynamic by their nature. Clusters typically contain a static number of processors and resources; resources come and go on the grid. Resources are provisioned onto and removed from the grid on an ongoing basis.

Grids are inherently distributed over a local, metropolitan, or wide-area network. Usually, clusters are physically contained in the same complex in a single location; grids can be (and are) located everywhere. Cluster interconnect technology delivers extremely low network latency, which can cause problems if clusters are not close together.

Grids offer increased scalability. Physical proximity and network latency limit the ability of clusters to scale out; due to their dynamic nature, grids offer the promise of high scalability.

For example, recently, IBM, United Devices, and multiple life-science partners completed a grid project designed to identify promising drug compounds to treat smallpox. The grid consisted of approximately two million personal computers. Using conventional means, the project most probably would have taken several years -- on the grid it took six months. Imagine what could have happened if there had been 20 million PCs on the grid. Taken to the extreme, the smallpox project could have been completed in minutes.

Cluster and grid computing are completely complementary; many grids incorporate clusters among the resources they manage. Indeed, a grid user may be unaware that his workload is in fact being executed on a remote cluster. And while there are differences between grids and clusters, these differences afford them an important relationship because there will always be a place for clusters -- certain problems will always require a tight coupling of processors.

However, as networking capability and bandwidth advances, problems that were previously the exclusive domain of cluster computing will be solvable by grid computing. It is vital to comprehend the balance between the inherent scalability of grids and the performance advantages of tightly coupled interconnections that clusters offer.

Quoted from