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What is
grid computing
and how does
it work?

Author: Shane Schick

If you are asking yourself, "What is grid computing?" you are hardly alone. Though the concept has been around for decades in the IT world, it was mostly considered a niche architecture with limited application outside of some specific high performance uses. Now that cloud computing is widely used and available, all that has changed; it is time to make more organizations aware of just how it works or how it can be used to accomplish difficult computing tasks.

The old adage that there's "strength in numbers" doesn't just apply to people. As defined by a standards body known as the Open Grid Forum, grid computing is an approach that "enables organizations to share computing and information resources across department and organizational boundaries in a secure, highly efficient manner." Think of grid computing as a way to apply the multiplier effect to computer and cloud computing resources.

What is grid computing and how does grid computing work?

People often picture a grid as a square or rectangle that's connected by a series of lines, or possibly like an electrical grid, where homes and businesses are connected to common power lines. In computing, though, the grid is made up of a set of hardware and software resources that may be geographically separated but connected over a network through specialized applications.

Think of each computing system or "node" in a grid as the member of a team that the software is leading. Each node may be assigned tasks or subtasks that they all work on at the same time. They might also communicate and share information over the network so that a specific end result can be achieved.

The networks involved in grid computing are parallel, in the sense that nodes are working alongside each other, even though they are distributed over possibly many nodes that are physically separated. The grid architecture and specialized orchestration software that manages it, makes sure the grid resources are used as effectively and efficiently as possible to get the desired results.

Types of grids and benefits

Depending on an organization's need, grid computing can be deployed in a number of ways.

Computational grids use the systems' collective computing power to analyze massive amounts of data. One example is where scientists have joined forces around the world and used computational grids to pursue research in areas such as physics or medical research, according to a paper from the International Symposium on Grid Computing. Others have applied grid computing to attempt mathematical calculations that would be nearly impossible using traditional IT resources, according to CERN, the European Organization for Nuclear Research.

Another common use for grids are called data grids, where massive amounts of data are distributed across multiple systems and locations, yet the data can be shared and accessed as though it seems to be in a single location. Splitting the data across a data grid can minimize the risk of data loss and make it easy to recover in case of a disaster. If one part of the data grid fails, copies in another location can be used to recover the data.

Utility grids offer a similar benefit. In the event that demands on an application suddenly spike, the distributed nature of grid computing allows additional resources to be added instantly to offset the demand.

This also builds in fault tolerance, where parts of the grid that are busy can offload some tasks to other systems to balance the load.

Whether trying to create models that predict the weather or search for life on other planets, there are always going to be more ambitious, innovative projects that put a strain on IT resources. Grid computing is one way to distribute the load while also using expensive computing resources more effectively.

Learn how to use network as a service (NaaS) solutions to support grid computing architectures and deliver the underlying network needed to properly distribute the resources to help produce results for innovative technology projects.