When we want to solve a computing problem that requires resources more than one computer can handle, we need to connect it with other computers and get the job done. Also, we do not come across such problems always. We need a dynamic way to connect the computers only when required. This helps us to achieve optimum utilization of resources. Grid and cluster computing are two such methods that help us to nail down mammoth computing tasks by connecting computers together. In this article, we delve on the differences and similarities between these two strategies. Grid computing is that technology in which we utilize the resources of many computers in a network towards solving a single computing problem at the same time, i.e., in parallel. Thousands of computers are employed in the process. All the devices that have computing power like desktops, laptops, tablets, mobiles, supercomputers, mainframes, servers, and meteorological sensors are connected together to form a single network. They are all connected together using the Internet. A software that is capable of dividing the program over many computers is used for this purpose. This entire infrastructure of connected computers is called a grid. When we are solving a problem that requires a lot of CPU processing or access to a huge repository of data, we use grid computing. If you want to solve a computing problem, you just have to log in to the Internet, and you can avail all the computing power you need to solve it. You don't have to worry from which part of the world it comes from. Just know that the required power and storage capacity is there, awaiting your perusal. Although the grid connects several different platforms, it can be accessed from a single web browser. The problem that needs to be solved is not within the capacity of a single computer. Hence, we harness the idle capacity of other computers and solve it. SETI (Search for Extraterrestrial Intelligence) @Home is a popular grid computing project. In this project, PC users from all over the world voluntarily give their idle CPU cycles and thus collaborate in analyzing signals coming from the outer space so that we can get evidence of extraterrestrial life (aliens). The grid so formed performs an important task and also saves money and utilizes optimum resources. In cluster computing, we connect a group of 2 or more computers in such a way that they work in combination as one computing unit towards solving a common computing problem. Similar computing components are connected together using a fast Local Area Network. This approach is useful in parallel processing, load balancing, and fault tolerance.

• In parallel processing, we utilize the processing power of two or more CPUs at the same time to achieve a common computing goal. As explained earlier, in cluster computing, we do just that.
• In fault tolerance, we have a system that is robust enough to handle hardware or software failures. Even if one node fails, the operation can be completed by other nodes. The computing power of the cluster gets affected when one system is at fault, but nevertheless, other systems are still available to perform the operation.
• In load balancing, we distribute computing tasks over the cluster such that one single machine is not burdened with heavy processing that it can't do.

Oracle Parallel Server is an example of a database cluster. Beowulf is another high-performance cluster.

• Both the techniques involve solving computing problems that are not within the scope of a single computer by connecting computers together.
• These two techniques want to increase efficiency and throughput by networking of computers.
• These two techniques save money because they achieve optimum utilization of resources.