Grid Computing
Some consider this to be the "the third it wave" following the Internet and Web, and you will be the backbone of another generation of services and applications that will further the study and development of GIS and related areas.
Grid computing permits the sharing of processing power, enabling the attainment of high performances in computing, management and services. Grid computing, (unlike the traditional supercomputer that does parallel computing by linking multiple processors over something bus) uses a network of computers to execute a program. The problem of using multiple computers is based on the issue of dividing up the tasks among the computers, without having to reference portions of the code being executed on other CPUs.
Parallel processing
Parallel processing may be the usage of multiple CPU's to execute different parts of an application together. Remote sensing and surveying equipment have already been providing vast amounts of spatial information, and how to manage, process or get rid of this data have become major issues in the field of Geographic Information Science (GIS).
To resolve these problems there has been much research in to the area of parallel processing of GIS information. This calls for the utilization of an individual computer with multiple processors or multiple computers that are connected over a network working on the same task. There are various forms of distributed computing, two of the most typical are clustering and grid processing.
The primary known reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide concurrency (do multiple things simultaneously).
Taking advantage of non-local resources - using available computing resources on a wide area network, or even the Internet when local computing resources are scarce.
Cost savings - using multiple cheap computing resources instead of spending money on time on a supercomputer.
Overcoming memory constraints - single computers have very finite memory resources. For large problems, using the memories of multiple computers may overcome this obstacle.
Limits to serial computing - both physical and practical reasons pose significant constraints to simply building ever faster serial computers.
Limits to miniaturization - processor technology is allowing an increasing number of transistors to be positioned on a chip.
However, despite having molecular or atomic-level components, a limit will undoubtedly be reached on how small components could be.
Economic limitations - it is increasingly expensive to create a single processor faster. Utilizing a larger amount of moderately fast commodity processors to achieve the same (or better) performance is less expensive.
The future: in the past 10 years, the trends indicated by ever faster networks, distributed systems, and multi-processor computer architectures (even at the desktop level) clearly show that parallelism may be the future of computing.
Distributed GIS
As the development of GIS sciences and technologies go further, increasingly quantity of geospatial and non-spatial data are involved in GISs due to more diverse data sources and development of data collection technologies. GIS data are generally geographically and logically distributed along with GIS functions and services do. Spatial analysis and Geocomputation are receiving more complex and computationally intensive. Sharing and collaboration among geographically dispersed users with various disciplines with various purposes are getting more necessary and common. A dynamic collaborative model " Middleware" is required for GIS application.
Computational Grid is introduced just as one solution for the next generation of GIS. Basically, the Grid computing concept is supposed make it possible for coordinate resource sharing and problem solving in dynamic, multi-organizational virtual organizations by linking computing resources with high-performance networks. Grid computing technology represents a fresh method of collaborative computing and problem solving in data intensive and computationally intensive environment and has the opportunity to satisfy all the requirements of a distributed, high-performance and collaborative GIS. Some methodologies and Grid computing technologies as solutions of requirements and challenges are introduced to enable this distributed, parallel, and high-throughput, collaborative GIS application.
Security
Security issues in such a wide area distributed GIS is crucial, which includes authentication and authorization using community policies and also allowing local control of resource. Discover more here (GSI), coupled with GridFTP protocol, makes certain that sharing and transfer of geospatial data and Geoprocessing are secure in the Computational Grid environment.
Conclusion
Because the conclusion, Grid computing has the chance to lead GIS into a new "Grid-enabled GIS" age with regard to computing paradigm, resource sharing pattern and online collaboration.