Grid Computing
Some think about this to be the "the third it wave" following the Internet and Web, and will be the backbone of the next generation of services and applications that are going to 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 conventional supercomputer that does parallel computing by linking multiple processors over a system bus) runs on the network of computers to execute an application. The problem of using multiple computers is based on the issue of dividing up the tasks on the list of computers, and never have to reference portions of the code being executed on other CPUs.
Parallel processing
Parallel processing is the use of multiple CPU's to execute different parts of a program together. Remote sensing and surveying equipment have already been providing vast levels of spatial information, and how to manage, process or dispose of this data have grown to be major issues in neuro-scientific Geographic Information Science (GIS).
To resolve these problems there has been much research into the area of parallel processing of GIS information. This involves the utilization of an individual computer with multiple processors or multiple computers that are connected over a network focusing on the same task. There are numerous types of distributed computing, two of the most typical are clustering and grid processing.
The primary reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide concurrency (do multiple things at the same time).
Benefiting from non-local resources - using available computing resources on a wide area network, or even the web when local computing resources are scarce.
Cost benefits - using multiple cheap computing resources rather than paying for 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 placed 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 really is increasingly expensive to make a single processor faster. Using a larger amount of moderately fast commodity processors to achieve the same (or better) performance is less costly.
The future: during the past a decade, 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
Because the development of GIS sciences and technologies go further, increasingly amount of geospatial and non-spatial data get excited about GISs because of more diverse data sources and development of data collection technologies. GIS data are usually geographically and logically distributed and GIS functions and services do. Spatial analysis and Geocomputation are getting more technical and computationally intensive. Sharing and https://buildinginformationmodelling.uk/best-scan-to-bim-bristol/ 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 intended to enable 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 approach to collaborative computing and problem solving in data intensive and computationally intensive environment and contains the chance 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 include authentication and authorization using community policies as well as allowing local control of resource. Grid Security Infrastructure (GSI), combined 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 gets the possiblity to lead GIS into a new "Grid-enabled GIS" age when it comes to computing paradigm, resource sharing pattern and online collaboration.