Grid Computing Some think about this to function as the third information technology wave after 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 something bus) uses a network of computers to execute a program. The issue of using multiple computers is based on the difficulty of dividing up the tasks among the computers, without having 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 sections of a program together. Drone Surveys Swindon and surveying equipment have already been providing vast amounts of spatial information, and how exactly to manage, process or get rid of this data have grown to be major issues in the field of Geographic Information Science (GIS). To solve these problems there has been much research into the section of parallel processing of GIS information. This calls for 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 many different types of distributed computing, two of the most typical 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). Benefiting from non-local resources - using available computing resources on a broad 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, 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 number of transistors to be positioned on a chip. However, even with molecular or atomic-level components, a limit will be reached on how small components could be. Economic limitations - it really is increasingly expensive to produce a single processor faster. Using a larger amount of moderately fast commodity processors to achieve the same (or better) performance is less expensive. 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 show that parallelism may be 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 due to more diverse data sources and development of data collection technologies. GIS data are usually geographically and logically distributed in addition to GIS functions and services do. Spatial analysis and Geocomputation are getting 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 just as one solution for another generation of GIS. Basically, the Grid computing concept is supposed 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 new method of collaborative computing and problem solving in data intensive and computationally intensive environment and has the opportunity to satisfy all of 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 together with 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 gets the chance to lead GIS right into a new Grid-enabled GIS age when it comes to computing paradigm, resource sharing pattern and online collaboration.
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