Grid Computing <p> <br /> <br />
Some consider this the "third wave of information" after the Internet and Web, and will become the backbone of the next generation of services and applications that go to research and develop GIS and related areas continue to be. <br /> <br />
Grid computing provides sharing of power, thereby achieving a high efficiency in computer science, management and services. Grid computing, (unlike the conventional supercomputer that does parallel computing by linking multiple processors over a system bus) uses a network of computers to carry out a program. The problem of using multiple computers lies in the difficulty of dividing the tasks among the computers, to refer to parts of the code to run on other CPUs. <br /> <br />
Parallel processing <br /> <br />
Parallel processing using multiple CPUs to work together to implement various parts of a program. Remote sensing and surveying equipment are huge amounts of spatial information, and how to manage, process or dispose of these data are important issues in the field of Geographic Information Systems (GIS). <br /> <br />
To solve these problems, there is much research on the area of parallel processing of GIS information. This includes the use of a computer with multiple processors or multiple computers that are connected via a network to work on the same task. There are many different types of distributed computing, are two of the most common clustering and grid processing. <br /> <br />
The main reasons for using parallel computing are: <br /> <br />
Saves time. <br /> <br />
Larger problems. <br /> <br />
Provide concurrency (do multiple things simultaneously). <br /> <br />
Taking advantage of the non-local sources - use of available IT resources in a wide area network or the Internet even when the local IT resources are scarce. <br /> <br />
Cost savings - using multiple "cheap" computing resources instead of paying for time on a supercomputer. <br /> <br />
Overcoming memory constraints - single computers have a very limited memory resources. For large problems, using the memories of multiple computers can overcome this obstacle. <br /> <br />
Limits to serial computing - both physical and practical reasons are important limitations to simply building ever faster serial computers. <br /> <br />
Limits to miniaturization - processor technology is enabling an increasing number of transistors placed on a chip. <br /> <br />
But even with molecular or atomic level components, a limit must be reached on how small parts can be. <br /> <br />
Economic constraints - is becoming increasingly expensive for a single processor faster. Using a larger number of relatively fast commodity processors the same (or better) performance is less expensive to reach. <br /> <br />
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 shows that parallelism is the future of computing. <br /> <br />
Distributed GIS <br /> <br />
As the development of GIS science and technology continue, more amount of geospatial and non-spatial data are involved in GIS due to more diverse data sources and developing technologies for data collection. GIS data appear to be geographically and logically distributed as GIS functions and services do. Geocomputation and spatial analysis are becoming increasingly complex and computationally intensive. Sharing and collaboration among geographically dispersed users with different disciplines with different purposes are becoming increasingly common and necessary. A dynamic collaborative model - "Middleware" - required for GIS application. <br /> <br />
Computational Grid is introduced as a possible solution for the next generation of GIS. In principle, the concept of Grid computing aims to coordinate resource sharing and problem solving in dynamic, multi-organizational virtual organizations by linking IT resources with a high-performance networks. Grid computing technology represents a new approach to collaborative computing and problem solving in data intensive and computationally intensive environment and has the chance to meet all requirements of a distributed, high performance and collaborative GIS. Some methods and Grid computing technologies and solutions to the needs and challenges are introduced to facilitate this distributed parallel and high-throughput, collaborative GIS application. <br /> <br />
Security <br /> <br />
Safety in such a large area distributed GIS is crucial that authentication and authorization using the Community policies and by local control of the source information. Grid Security Infrastructure (GSI), combined with the GridFTP protocol, enables the sharing and transfer of geospatial data and Geoprocessing are safe in the Computational Grid environment. <br /> <br />
Conclusion <br /> <br />
As a conclusion, Grid computing is the chance to lead GIS into a new Grid-enabled GIS "in terms of age computing paradigm, resource sharing pattern and online collaboration. <br /> </ P>