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.