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Grid computing--also called computing on demand--has the potential to change the computing model we've used for more than a half of a century. Computers have essentially been self-contained systems with local memory, I/O and storage. This model has been expanded and refined over the years while remaining essentially unchanged.
Of course, there have been numerous computer revolutions in the last five decades: networked computers on LANs; two-tier computing or client-server computing, which separates the application from the data; three-tier computing that separates the user interface from the applications and the data such as Java. And the short list of giant leaps in computing would certainly need to include the Internet, WWW, SANs and high- performance or massively parallel computers.
Yet in spite of these admittedly significant advances, the basic model remained the same. Grid computing is the first real change to the model that leverages these advances. For the first time, users will be able to get the computing they need, on demand, without having to know anything about computing. Computing on demand will have a huge impact on the way data is used, shared and stored.
|Grid Computing's Impact on storage|
Internet on steroids
Some have called grid computing the Internet on steroids. In reality, it's the virtualization of computing to the user. Grid computing allows IT organizations to forego having to provide individual computers to provide computational capabilities.
The Global Grid Forum and the IETF for Grid
Computing define the grid as a type of parallel and distributed system that enables the sharing, selection and aggregation of resources distributed across multiple administrative domains based on their availability, capability, performance, cost and users' QoS requirements.
Grid computing is often confused with cluster computing. If distributed resources happen to be managed by a single, global centralized scheduling system, then it's a cluster. In a cluster, all nodes work cooperatively with a common goal and objective as a centralized, global resource manager performs the resource allocation. In a grid, each node has its own resource manager and allocation policy.
What this means is that applications will be able to dynamically draw processing cycles, memory, I/O and storage from anywhere on the grid based on its moment-by-moment requirements and resource availability. The grid continuously identifies computer processing, I/O, memory or storage demand and assigns the appropriate computer resources to fulfill that demand.
The compute resources on the grid must be interconnected across a high-speed, high-performance network. These resources can be local or geographically distributed. The grid resource allocation software takes into account the physical location of the resources before assigning them. Bandwidth and performance to each compute resource have an impact on the decision algorithm.
Grid computing has enormous potential and has captured the attention of the computer industry as well as a fistful of research dollars from companies such as Fujitsu Softek, Hewlett-Packard, IBM, Microsoft, Siemens, Sun Microsystems and others. Grid computing has the potential to reduce research time by orders of magnitude. It could allow cancer researchers to test thousands of drug candidates in minutes vs. years, or astronomers to map all near earth objects in months vs. decades or provide meteorologists with an incredibly accurate forecast of any given spot in the world. Grid computing is at the same point that the World Wide Web was in 1991.
That sounds almost too good to be true, but today there are dozens of working grid projects around the world. Of course, there are still many significant challenges ahead to take grid computing beyond the do-it-yourself experiment stage to commercially shrink-wrapped viability. Perhaps the most important challenge for grid computing will be how to manage the storage.
This was first published in August 2003