Grid Computing In Distributed GIS

Grid Computing

Some consider this to be the "the third information technology wave" after the Internet and Web, and you 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.

Click hereClick here for info  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 a system bus) runs on the 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, and never have 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 solve these problems there's been much research in to the area of parallel processing of GIS information. This involves the utilization of a single computer with multiple processors or multiple computers which are connected over a network focusing on the same task. There are numerous 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 simultaneously).

Benefiting from non-local resources - using available computing resources on a broad area network, or even the Internet 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 what small components can be.

Economic limitations - it is increasingly expensive to make a single processor faster. Utilizing a larger amount of moderately fast commodity processors to achieve the same (or better) performance is less costly.

The future: during 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 amount 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 usually geographically and logically distributed and 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 receiving more necessary and common. A dynamic collaborative model " Middleware" is necessary for GIS application.

Computational Grid is introduced as a possible solution for another 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 method of collaborative computing and problem solving in data intensive and computationally intensive environment and has 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 critical, which includes authentication and authorization using community policies along with 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 has the possiblity to lead GIS into a new "Grid-enabled GIS" age with regards to computing paradigm, resource sharing pattern and online collaboration.