Grid Computing
Some consider this to function as "the third information technology wave" after the Internet and Web, and will be the backbone of another 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 something bus) uses a network of computers to execute an application. The problem of using multiple computers is based on the issue of dividing up the tasks among the computers, without needing 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 sections of a program together. Remote sensing and surveying equipment have been providing vast levels of spatial information, and how to manage, process or get rid of this data have grown to be major issues in the field of Geographic Information Science (GIS).
To solve these problems there has been much research in to the section of parallel processing of GIS information. This calls for the utilization of a single computer with multiple processors or multiple computers that are connected over a network focusing on the same task. There are various types of distributed computing, two of the most common are clustering and grid processing.
The primary known reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide https://aerial-lidar.co.uk/best-3d-modelling/ (do multiple things simultaneously).
Benefiting from non-local resources - using available computing resources on a wide area network, as well as the web when local computing resources are scarce.
Cost savings - using multiple cheap computing resources instead of 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 a growing number of transistors to be placed on a chip.
However, even with molecular or atomic-level components, a limit will be reached on how small components could be.
Economic limitations - it really is increasingly expensive to produce a single processor faster. Using a larger number of moderately fast commodity processors to attain 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
Because the development of GIS sciences and technologies go further, increasingly level of geospatial and non-spatial data are involved in GISs because of more diverse data sources and development of data collection technologies. GIS data tend to be geographically and logically distributed and also GIS functions and services do. Spatial analysis and Geocomputation are receiving more technical and computationally intensive. Sharing and collaboration among geographically dispersed users with various disciplines with various purposes are receiving more necessary and common. A dynamic collaborative model " Middleware" is required for GIS application.
Computational Grid is introduced as a possible solution for the next generation of GIS. Basically, the Grid computing concept is intended 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 new approach to collaborative computing and problem solving in data intensive and computationally intensive environment and contains the chance to satisfy all of 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 that wide area distributed GIS is critical, 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
As 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.