Grid Computing
Some consider this to be the "the third information technology wave" after the Internet and Web, and you will be the backbone of the next generation of services and applications that are going to further the research and development of GIS and related areas.
https://aerial-lidar.co.uk/best-drone-surveys/ computing permits 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 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. Remote sensing and surveying equipment have already been providing vast levels 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's 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 that 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 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 wide area network, or 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 a growing amount of transistors to be placed on a chip.
However, despite having molecular or atomic-level components, a limit will undoubtedly be reached on how small components can be.
Economic limitations - it is increasingly expensive to generate a single processor faster. Utilizing a larger number of moderately fast commodity processors to achieve the same (or better) performance is less expensive.
The future: during 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
Because the development of GIS sciences and technologies go further, increasingly quantity 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 as well as 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 necessary 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 approach to collaborative computing and problem solving in data intensive and computationally intensive environment and has the chance 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 such a wide area distributed GIS is crucial, which includes authentication and authorization using community policies and 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 gets the possiblity to lead GIS right into a new "Grid-enabled GIS" age with regards to computing paradigm, resource sharing pattern and online collaboration.