Difference between Grid computing and cluster computing with example

When several computers are connected through a network, the true potential of computing can be unleashed, and depending upon, what exact task you want to accomplish, there must be a correct strategy to connect all the different computers. While the internet is the biggest network of computers, the same can be used to accomplish several tasks to solve several global problems or tasks that can’t be accomplished with a single computer. There are several tasks that require remarkably huge computing resources, more than even the combined effort of numerous supercomputers, and that’s when the concept of distributed computing comes to the picture, and I will discuss two distributed computing schemes here today.

I will be talking about grid computing and cluster computing, which are two popular modes of distributed computing, however, there are significant differences in how these two computing schemes are implemented, for your understanding. I will also be throwing light on how we use both grid computing and cluster computing for our everyday computing requirements. Several leading companies globally these days are trying to implement both grid computing and cluster computing to solve global problems by improving the architecture or by implementing necessary techniques to make both the distributed schemes more efficient.

So, without any further delay, let’s get started with the differences between grid computing and cluster computing, after explaining both in brief.

What is grid computing?

Grid computing is a distributed computing architecture where multiple computers are connected through one or multiple networks making it possible for all the individual nodes on the system to use each other’s hardware resources like processing power, memory, network bandwidth, and everything else.

All the computers connected to a grid computer network can be located either in the same place or can also be located across the globe connected through the internet or any other network. In grid computing, all the nodes do not necessarily need to have the same hardware resources, and each node can be assigned different jobs to accomplish one combined task.

Consider a grid computing architecture like that of several employees in a company with varying efficiency levels, each having expertise in something that others don’t. So, when a task needs to be accomplished by a team, all the employees working can give their best, and every employee can get help from the others if there is some task that somebody else can handle more efficiently. So everybody is maintaining communication with all the team members to complete the task, besides maintaining contact with the team leader or the senior who has assigned the task.

The same happens in grid computing where the central node assigns tasks to the other nodes, and the nodes can also communicate with each other besides the central mode to complete the tasks and deliver the final results to the central node.

Examples of grid computing

Grid computing is used all over the world, where complete tasks need to be carried out for seismology, health care, understanding complex, and random patterns that require a ton of computing power. If you have come across Folding@Home which is used to understand protein dynamics for healthcare research, it is also an example of grid computing where you can install the client for the project on your computer and make it a node to contribute to the development. At present, Folding@Home is quite popular among the geeks as it is being used extensively for COVID-19 research and also to find a vaccine or at least to find a proper treatment for the disease.

What is cluster computing?

Cluster computing is another example of distributed computing where all the nodes are assigned the same task and they are again connected to the cluster head that eventually assigns the tasks to the nodes. As all the nodes in a cluster computing architecture do the same task, the complete system can be considered as a single entity to perform a certain designated task. 

It is also necessary that all the nodes in the case of a cluster computing architecture should have the same hardware and operating system and in most cases, they are connected using a very fast network for lower response times. However, there are certain cluster computing architectures like Open Source Cluster Application Resources or OSCAR, where different hardware configurations can be used along with different operating systems.

As all the nodes in a cluster computing architecture require the fastest communication between each other, these devices are located at a single place and these nodes are implemented for tasks of smaller scales. However, there can be other ways to connect multiple nodes and cluster heads across the internet but all the nodes under a single cluster head should be placed very closely for better efficiency of every individual cluster head to accomplish the final task.

Examples of cluster computing

Cluster computing is also used by several organizations and enterprises to complete the tasks. Additionally, cluster computing is also used in several gaming consoles that implement multiple graphics cards to carry out faster and more precise calculations when it comes to gaming or other highly intensive algorithms.

Differences between grid and cluster computing

Difference between Grid computing and cluster computing with example

Now, let’s have a look at the differences between grid computing and cluster computing more closely, besides the basic architectural difference that I just mentioned above.

Type of computers required: As I have already discussed, in grid computing, all the computers connected to the system don’t necessarily need to have the same hardware resources and the nodes should be powered by the same software or operating system. However, in the case of cluster computing, except in certain situations with specialized software tools and tweaks, all the computers need to run on the same hardware and they should be powered by the same operating system to function at all.

Tasks that can be done: In the case of cluster computing, all the nodes are assigned to carry out the same set of tasks, as I have already discussed. In the case of grid computing, as well, all the computers can be assigned to do the same set of tasks, however, depending upon the configuration of an individual node, that particular node can also be assigned a different set of tasks if there are some unused resources that can be used to fast-track the overall accomplishment of the process.

Location of the nodes: In most cases, the nodes in a grid computing setup are located across the globe and they are connected using the internet or any other slow network bus for interconnection and communicating with the central node. On the other hand in the case of cluster computing, all the nodes are connected with each other through a very fast network, and that is implemented by placing all the nodes locally, most likely in the same location.

Type of topology used: In the case of grid computing, all the computers can behave independently and the tasks are controlled by the control node however the complete grid computing setup is a decentralized architecture unlike that of cluster computing. In the case of cluster computing, all the computers are connected through a centralized node and if there are several nodes, they are connected to each other through a cluster head.

Scheduling of tasks: As grid computing is a decentralized setup, there can be multiple servers if necessary, and there can be no server at all. Thus, all the nodes in a grid computing act independently and there is no such scheduling that is carried out. On the other hand, in the case of cluster computing, the tasks are scheduled by a centralized server.

Resource management: As all the computers in a grid computing setup have a different set of resources, the resources are not managed by any centralized server, and each node manages its own set of resources. In the case of cluster computing, however, there is a centralized resource manager at the central node that manages all the resources within the nodes for improved efficiency and workflow.

Both grid computing and cluster computing are the two highly powerful computing modes that can accomplish a certain task with a lot of accuracies and within a short span of time. Several leading organizations across the globe are using the power of grid and cluster computing to solve complex problems of mankind, and also for improving the state of artificial intelligence.

So, that was all about grid computing and cluster computing, along with the differences between the two. Do you have any questions? Feel free to comment on the same below.

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