Grid Computing
Some think about this to function as "the third information technology wave" after the Internet and Web, and you 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 allows for 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) runs on the network of computers to execute a program. The issue of using multiple computers is based on the difficulty 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 may be the use of multiple CPU's to execute different sections of an application together. Remote sensing and surveying equipment have already been providing vast amounts of spatial information, and how to manage, process or dispose of this data have become major issues in neuro-scientific Geographic Information Science (GIS).
To solve these problems there has been much research into the section 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 many different forms 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 as well).
Benefiting from non-local resources - using available computing resources on a broad area network, as well as the Internet 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, utilizing 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 amount of transistors to be positioned on a chip.
However, despite having molecular or atomic-level components, a limit will be reached on what small components can be.
Economic limitations - it really is increasingly expensive to generate 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 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 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 are generally geographically and logically distributed and also GIS functions and services do. Spatial analysis and Geocomputation are getting 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 as a possible solution for the next generation of GIS. Basically, the Grid computing concept is supposed 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 new approach to collaborative computing and problem solving in data intensive and computationally intensive environment and contains the opportunity to satisfy all the requirements of a distributed, high-performance and collaborative GIS. Find more information and Grid computing technologies as solutions of requirements and challenges are introduced make it possible for this distributed, parallel, and high-throughput, collaborative GIS application.
Security
Security issues in such a wide area distributed GIS is critical, which includes authentication and authorization using community policies and also allowing local control of resource. Grid Security Infrastructure (GSI), coupled 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 has the possiblity to lead GIS right into a new "Grid-enabled GIS" age regarding computing paradigm, resource sharing pattern and online collaboration.