Advantages and Disadvantages of Cluster and Grid Computing
In Load-Balancing Clusters, requests to a particular server are redirected to other servers working together to distribute and reduce the workload on a server resulting in higher speeds.
Cluster Computing is manageable and easy to implement. The underlying architecture (Single System Image) is user friendly, and the user is able to view the entire entity as a single unit without worrying about the infrastructure. There is a high availability of resources meaning that if one node fails, another node takes over. Cluster computing allows for expandability, and you can add more and more nodes into the system. However, Cluster Computing is not very suitable for business and commercial purposes. (Credit Collection Services, for example, would be ill-suited for this). It requires specialized knowledge of system and programming skills and languages that are not often used commercially, such as Fortran, Ada, Python, and C++. In Cluster Computing, there are no free resources in the sense that one must submit a particular task into a queue and wait for the results.
Grid Computing
Grid Computing is where resources from several domains are separated to solve problems that are unsolvable using the processing of one computer. The term Grid Computing is derived from the concept of power grids. To switch on a device, all you need is to plug it to the closest power source. Once you do this, you can operate your device regardless of where the power is generated from, the paths used by electrical currents, or the processes involved in delivering electricity to your machine.
Grid computing is similar to power grids in the sense that users can access computer resources with little to no knowledge of their location, the underlying infrastructures, software, operating systems, or hardware. Grid computing is different from cluster computing in that grids can be scattered across the world and do not need to be on-premises like clusters. This is because grids use the internet to connect resources and, therefore, do not need to be in the same physical and geographical area. This shifts the priority from performance in cluster computing to a preference of resource sharing, thus doing away with the need to have a Single System Image (SSI) since long devices on the grid are diversified and scattered across different geographical areas.
Grid Computing is mainly used in education and research. Some of the most common applications of grid computing involve artificial intelligence and computation, computational aerodynamics, computer vision, remote sensing applications, pattern recognition, data visualization, image processing, medical imaging, polymer chemistry, quantum mechanics, nuclear weapon design, nuclear reactor safety, astrophysics, and weather forecasting.
Classification of Grids
Grids can be grouped according to three categories: Computational, Scavenging, and Data Grids. Computation grids prioritize on computation, and thus problems allocated to this category of grids have high performance and computation requirements. Scavenging grids, on the other hand, locate and utilizes machine cycles on idle computers and servers for resource and computer-intensive tasks. Data grids provide an integrated interface for all data archives in an organization and where data can be analyzed, managed, and secured.