Tech Geeks

Grid Computing

Some consider it the “third wave of information technology”, after the Internet and Web, and the backbone of the next generation of services and applications that go on the discovery and development of GIS and related areas are more.

Grid computing enables the sharing of computing power, so that the attainment of high achievement in computer science, management and services. Grid computing (and in contrast to traditional supercomputers, parallel computing by linking multiple processors in a system bus) uses a network of computers to run a program. The problem of using multiple computers is the difficulty of the division of tasks between computers, without reference parts of the code running on other CPUs.

Parallel processing

Parallel processing is the use of multiple CPU’s to run in different areas of a program together. Remote Sensing and Surveying already offer large amounts of spatial data have, and how to manage, process or dispose of this data have become major issues in the field of Geographic Information Science (GIS).

To solve these problems has been much research in the area of parallel processing of GIS information. This includes the use of a single computer with multiple processors or multiple computers connected over a network on the same task are. There are many different types of distributed computing, are two of the most common clustering and grid processing.

The main reasons for the use of parallel computing are:

Saves time.

Solve major problems.

Enter Concurrency (do several things at the same time).

Taking advantage of non-local resources – use of existing IT resources over a Wide Area Network, or even the Internet if local IT resources are scarce.

Cost savings – using multiple “cheap” computing resources instead of paying for time on a supercomputer.

Overcoming the memory constraints – single computers have very finite memory resources. For large problems, with the memories of multiple computers, this hurdle to overcome.

Limits to serial computing – both physical and practical reasons, some important limitations, simply building ever faster serial computers.

Limits of miniaturization – processor technology is therefore an increasing number of transistors placed on a chip.

But even with molecular or atomic level components, a limit on how small components can be achieved.

Economic constraints – it is always more expensive to make a single processor faster. With a larger number of moderately fast commodity processors to achieve the same (or better) performance is less expensive.

The future: in the last 10 years, the trends were always 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 are continuing, involving more amount of spatial data and non-spatial data in GIS due to various data sources and development of technologies, data collection. GIS data do tend to spread geographically and logically, and GIS functions and services. Spatial Analysis and Geocomputation become more complex and computationally intensive. Sharing and collaboration between geographically dispersed users with different disciplines with different purposes are always necessary and customary. 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 to enable, coordinate and share resources, problem solving in dynamic, multi-organizational virtual organizations by linking IT resources with high-performance networks. Grid computing technology provides a new approach to collaborative computing and problem solving in data intensive and compute-intensive environment and has the chance to meet all the requirements of a distributed high-performance and collaborative GIS. Some methods and technologies such as grid computing solutions from needs and challenges are introduced, so that this distributed, parallel and high-throughput, collaborative GIS application.

Security

Security issues in such a large area distributed GIS is critical, including authentication and authorization by the Community policies and the possibility of local control of resources. Grid Security Infrastructure (GSI), combined with the GridFTP protocol, ensures that the sharing and dissemination of geodata and geoprocessing are secure in the computational grid environment.

Conclusion

As a result, grid computing has the chance to lead GIS into a new “Grid-enabled GIS” age in terms of computing paradigm, resource sharing and online collaboration patterns.