Grid Computing
Some think about this to function as "the third information technology wave" following the Internet and Web, and will be the backbone of another generation of services and applications that are going to further the research 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 traditional supercomputer that does parallel computing by linking multiple processors over a system bus) runs on the network of computers to execute a program. The problem of using multiple computers is based on the difficulty of dividing up the tasks on the list of computers, and never have to reference portions of the code being executed on other CPUs.
Parallel processing
Parallel processing is the usage of multiple CPU's to execute different parts of an application together. Remote sensing and surveying equipment have been providing vast levels of spatial information, and how exactly to manage, process or dispose of this data have become major issues in the field of Geographic Information Science (GIS).
To resolve these problems there's been much research in to the section of parallel processing of GIS information. This involves 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 numerous forms 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 as well).
Taking advantage of non-local resources - using available computing resources on a wide area network, and even the web 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, 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 can be.
Economic limitations - 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 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 is 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 because of more diverse data sources and development of data collection technologies. GIS data are usually geographically and logically distributed and also GIS functions and services do. Spatial analysis and Geocomputation are getting 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.
https://aerial-lidar.co.uk/best-drone-surveys/ is introduced just as one solution for another 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 fresh method of collaborative computing and problem solving in data intensive and computationally intensive environment and has 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 such a wide area distributed GIS is critical, which includes authentication and authorization using community policies and also allowing local control of resource. Grid Security Infrastructure (GSI), combined 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 possiblity to lead GIS into a new "Grid-enabled GIS" age in terms of computing paradigm, resource sharing pattern and online collaboration.