What is Enterprise Computing?
Grid Computing History
Grid Terminology
Grid vs. Distributed Computing
Grid vs. Clustering
Grid vs. Utility Computing
  Oracle "Grid Overview" (1 min.)
  eWeek - "Grid in the Enterprise"
InfoWorld - "Getting Down to Grid Computing"
Computerworld - "Grid Poised for Primetime"
MIT Sloan - "Grid Computing"
  Oracle Grid Computing
Oracle 10g: Infrastructure for Grid
IBM: Fundamentals to Grid Computing
IBM: The Era of Grid Computing


Clustering is the use of multiple computers, typically PCs or UNIX workstations, multiple storage devices, and redundant interconnections, to form what appears to users as a single, highly-available system. Cluster computing can be used for load balancing as well as for high availability. Advocates of clustering suggest that the approach can help an enterprise achieve 99.999% availability in some cases. One of the main ideas of cluster computing is that, to the outside world, the cluster appears to be a single system.

This differs from Enterprise Grid Computing where resources can enter and leave the pool as necessary.

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.

Although these workday tasks are clustering's greatest hits, another application often gets more press: grid computing. The two terms are often used interchangeably — both involve multiple systems working together to carry out a similar set of functions — but there are differences. You can think of a cluster as grid computing under one roof: One company or department sets up a cluster and controls the whole, usually localized or centralized, system.

Grid computing is more far-reaching; individual systems can be added or subtracted without a central control. What's more, miles can separate grid participants as long as there's a network connection between them. An example on a massive — nay, cosmic — scale is the SETI@Home project, which enlists PC users all over the Internet to download a screen saver that uses extra clock cycles to sort through radioClustering.

In simple terms, clustering is the connecting together of two or more computers in a way that they behave like a single computer. Clustering refers to a number of ways to group servers in order to distribute load and eliminate single points of failure within a business-critical system.

Clustering solutions are employed for parallel processing, load-balancing and, most commonly, fault tolerance. Proponents of clustering suggest that the approach can help an enterprise achieve close to 100% availability in some cases. One of the attributes of clustering is that, to the outside observer, the cluster appears to be a single system.


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