Grid Computing

Some think about this to be the “the third information technology wave” following the Internet and Web, and will be the backbone of another generation of services and applications that will further the research 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 traditional 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 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 an application 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’s been much research in to the area of parallel processing of GIS information. Drone Surveys Worcestershire 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 frequent are clustering and grid processing.

The primary reasons for using parallel computing are:

Saves time.

Solve larger problems.

Provide concurrency (do multiple things concurrently).

Benefiting from non-local resources – using available computing resources on a wide area network, or even the Internet when local computing resources are scarce.

Cost benefits – using multiple cheap computing resources rather than spending money on 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 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. Using a larger amount of moderately fast commodity processors to attain the same (or better) performance is less expensive.

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 is 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 because of more diverse data sources and development of data collection technologies. GIS data are usually 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 receiving more necessary and common. A dynamic collaborative model ” Middleware” is required for GIS application.

Computational Grid is introduced as a possible solution for another 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 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 that 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 sure that sharing and transfer of geospatial data and Geoprocessing are secure in the Computational Grid environment.

Conclusion

Because the conclusion, Grid computing gets the chance to lead GIS into a new “Grid-enabled GIS” age with regard to computing paradigm, resource sharing pattern and online collaboration.