Grid Computing Some think about this to function as 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 research 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) runs on the network of computers to execute an application. Visit this site of using multiple computers lies in the difficulty 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 usage 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 grown to be major issues in neuro-scientific Geographic Information Science (GIS). To solve these problems there has been much research into the section of parallel processing of GIS information. This involves the utilization of a single computer with multiple processors or multiple computers that are connected over a network working on the same task. There are various forms of distributed computing, two of the most frequent are clustering and grid processing. The primary known reasons for using parallel computing are: Saves time. Solve larger problems. Provide concurrency (do multiple things at the same time). Benefiting from non-local resources - using available computing resources on a broad area network, and even the Internet when local computing resources are scarce. Cost savings - 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 placed on a chip. However, despite having molecular or atomic-level components, a limit will undoubtedly be reached on what 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 attain the same (or better) performance is less expensive. 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 is the future of computing. Distributed GIS As the development of GIS sciences and technologies go further, increasingly level of geospatial and non-spatial data are involved in GISs because of more diverse data sources and development of data collection technologies. GIS data are generally geographically and logically distributed in addition to 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 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 intended 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 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 to enable this distributed, parallel, and high-throughput, collaborative GIS application. Security Security issues in such a wide area distributed GIS is critical, which includes authentication and authorization using community policies as well as 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 As the conclusion, Grid computing has the chance to lead GIS into a new Grid-enabled GIS age regarding computing paradigm, resource sharing pattern and online collaboration.
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