Grid Computing Some think about this to be the the third it wave following the Internet and Web, and you will be the backbone of another generation of services and applications that are going to 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 traditional supercomputer that does parallel computing by linking multiple processors over a system bus) uses a network of computers to execute an application. The issue of using multiple computers lies in the difficulty of dividing up the tasks on the list of computers, without having 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 amounts of spatial information, and how to manage, process or dispose of this data have become major issues in neuro-scientific Geographic Information Science (GIS). To solve these problems there has been much research into the area of parallel processing of GIS information. This involves the utilization of an individual 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 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 broad area network, or even the web 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, 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 an increasing amount of transistors to be positioned on a chip. However, despite having molecular or atomic-level components, a limit will undoubtedly be reached on what small components can be. Economic limitations - it is increasingly expensive to produce a single processor faster. Utilizing https://surveyorhampshire.co.uk/best-utility-survey-hampshire/ of moderately fast commodity processors to attain the same (or better) performance is less costly. The future: during the past a decade, 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 level of geospatial and non-spatial data get excited about GISs due to more diverse data sources and development of data collection technologies. GIS data tend to be geographically and logically distributed and also 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 getting more necessary and common. 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 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 contains 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 crucial, which include authentication and authorization using community policies together with allowing local control of resource. Grid Security Infrastructure (GSI), coupled with GridFTP protocol, makes certain 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 regards to computing paradigm, resource sharing pattern and online collaboration.
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