Grid Computing In Distributed GIS
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Grid Computing
Some think about this to function as "the third it wave" following the Internet and Web, and 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 allows for 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) runs on the network of computers to execute a program. The problem of using multiple computers is based on the issue 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 an application 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 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 common are clustering and grid processing.
The primary reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide concurrency (do multiple things concurrently).
Benefiting from non-local resources - using available computing resources on a broad area network, as well as the web when local computing resources are scarce.
Drone Surveys Worcestershire - using multiple cheap computing resources instead of spending money on time on a supercomputer.
Overcoming memory constraints - single computers have very finite memory resources. For large problems, utilizing 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, even with 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 generate a single processor faster. Utilizing a larger amount of moderately fast commodity processors to attain the same (or better) performance is less costly.
The future: in 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
Because the development of GIS sciences and technologies go further, increasingly amount 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 and 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 getting more necessary and common. A dynamic collaborative model " Middleware" is necessary for GIS application.
Computational Grid is introduced just as one solution for the next 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 fresh approach to 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 critical, which include 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 has the possiblity to lead GIS right into a new "Grid-enabled GIS" age regarding computing paradigm, resource sharing pattern and online collaboration.
Some think about this to function as "the third it wave" following the Internet and Web, and 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 allows for 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) runs on the network of computers to execute a program. The problem of using multiple computers is based on the issue 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 an application 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 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 common are clustering and grid processing.
The primary reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide concurrency (do multiple things concurrently).
Benefiting from non-local resources - using available computing resources on a broad area network, as well as the web when local computing resources are scarce.
Drone Surveys Worcestershire - using multiple cheap computing resources instead of spending money on time on a supercomputer.
Overcoming memory constraints - single computers have very finite memory resources. For large problems, utilizing 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, even with 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 generate a single processor faster. Utilizing a larger amount of moderately fast commodity processors to attain the same (or better) performance is less costly.
The future: in 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
Because the development of GIS sciences and technologies go further, increasingly amount 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 and 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 getting more necessary and common. A dynamic collaborative model " Middleware" is necessary for GIS application.
Computational Grid is introduced just as one solution for the next 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 fresh approach to 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 critical, which include 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 has the possiblity to lead GIS right into a new "Grid-enabled GIS" age regarding computing paradigm, resource sharing pattern and online collaboration.
