Grid Computing In Distributed GIS
Grid Computing
Some think about this to be the "the third information technology wave" following the Internet and Web, and you will be the backbone of the next generation of services and applications that will further the study and development of GIS and related areas.
Grid computing permits 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) uses a network of computers to execute a program. The issue of using multiple computers lies in 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 use of multiple CPU's to execute different sections of a program together. Remote sensing and surveying equipment have already been providing vast levels of spatial information, and how exactly to manage, process or get rid of this data have become major issues in neuro-scientific Geographic Information Science (GIS).
To solve these problems there has been much research in to the section of parallel processing of GIS information. This calls for the utilization of a single computer with multiple processors or multiple computers that are connected over a network focusing on the same task. There are various forms of distributed computing, two of the most common are clustering and grid processing.
The primary known reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide concurrency (do multiple things simultaneously).
Taking advantage of non-local resources - using available computing resources on a wide area network, or even the Internet when local computing resources are scarce.
Cost savings - 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, 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 positioned on a chip.
However, even with molecular or atomic-level components, a limit will undoubtedly be reached on what small components can be.
https://buildinginformationmodelling.uk/best-scan-to-bim-gloucestershire/ - 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 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 is the future of computing.
Distributed GIS
As the development of GIS sciences and technologies go further, increasingly amount of geospatial and non-spatial data get excited about GISs because of more diverse data sources and development of data collection technologies. GIS data tend to be geographically and logically distributed along with GIS functions and services do. Spatial analysis and Geocomputation are receiving more technical 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 fresh 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. Some methodologies 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 that wide area distributed GIS is crucial, which includes authentication and authorization using community policies in addition to allowing local control of resource. Grid Security Infrastructure (GSI), coupled 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 has the possiblity to lead GIS right into a new "Grid-enabled GIS" age in terms of computing paradigm, resource sharing pattern and online collaboration.