Difference between Cluster Computing and Grid Computing



A cluster computer refers to a network of same type of computers whose target is to work as one collaborative unit. Such a network is used when a resource-hungry task requires high-computing power or memory. Two or more same types of computers are clubbed together to make a cluster and perform the task.

Grid computing refers to a network of same or different types of computers whose target is to provide an environment where a task can be performed by multiple computers together on need basis. Each computer can work independently as well.

Read through this article to find out more about Cluster Computing and Grid Computing and how they are different from each other.

What is Cluster Computing?

A computer cluster is a logical entity of many computers connected by a local area network (LAN). The connected computers function together as a single, far more powerful unit. A computer cluster offers significantly increased processing speed, storage capacity, data integrity, dependability, and resource availability.

Computer clusters are expensive to set up and maintain. When compared to a single computer, computer clusters require a substantially larger running overhead.

Many businesses employ computer clusters to improve processing speed, expand database capacity, and implement faster storage and retrieval strategies.

Computer clusters come in a variety of shapes and sizes, including −

  • Clusters for load balancing
  • Clusters with high availability (HA)
  • Clusters with high performance (HP)

When a company requires large-scale processing, the benefits of deploying computer clusters are apparent. Computer clusters provide the following benefits when deployed in this manner:

  • Cost-effectiveness − For the amount of power and processing speed produced, the cluster approach is cost-effective. It is both more efficient and less expensive than alternative options, such as putting up mainframe computers.

  • Speed of processing − Several high-speed computers work together to provide unified processing, which results in faster overall processing.

  • Improved network infrastructure −To construct a computer cluster, various LAN topologies are utilized. These networks create an infrastructure that is highly efficient and effective in avoiding bottlenecks.

  • High resource availability − If a single component in a computer cluster fails, the other machines process data without interruption. In mainframe systems, this redundancy is missing.

Computer clusters, unlike mainframe computers, can be modified to improve existing specs or add new components to the system.

What is Grid Computing?

Grid computing is a processing architecture that combines multiple computer resources to achieve a common goal. Grid computing allows computers over a network to collaborate on a job, effectively acting as a supercomputer.

A grid is typically used to perform numerous jobs inside a network, but it can also run specialized applications. It's made to address too big problems for a supercomputer while still handling a lot of smaller ones. Computing grids provide a multiuser architecture that can handle the sporadic needs of big data processing.

A grid is connected by parallel nodes that create a computer cluster that operates on a Linux or free software operating system. The cluster might be as tiny as a single workstation or as large as many networks.

Several computing resources apply the technology to various applications, including mathematical, scientific, and educational tasks. Structure analysis, Web services such as ATM banking, back-office infrastructures, and scientific or marketing research are all examples of where it's applied.

Grid computing consists of related programs in a parallel networking environment and solves computational computer issues. It connects each PC and merges data into a single computationally intensive program.

Grids have various resources based on multiple software and hardware structures, computer languages, and frameworks. These can be shared across a network or by following open standards with specified criteria to reach a common aim.

Difference between Cluster Computing and Grid Computing

The following table highlights the major differences between Cluster Computing and Grid Computing.

Key
Cluster Computing
Grid Computing
Computer Type
Nodes or computers have to be of the same type, like same CPU, same OS. Cluster computing needs a homogeneous network.
Nodes or computers can be of same or different types. Grid computers can have homogeneous or heterogeneous network.
Task
Computers of Cluster Computing are dedicated to a single task and they cannot be used to perform any other task.
Computers of Grid Computing can leverage the unused computing resources to do other tasks.
Location
Computers of Cluster computing are co-located and are connected by high speed network bus cables.
Computers of Grid Computing can be present at different locations and are usually connected by the Internet or a low speed network bus.
Topology
Cluster computing network is prepared using a centralized network topology.
Grid computing network is distributed and have a decentralized network topology.
Task Scheduling
A centralized server controls the scheduling of tasks in cluster computing.
In Grid Computing, multiple servers can exist. Each node behaves independently without the need of any centralized scheduling server.
Resource Manager
Cluster Computing network has a dedicated centralized resource manager, managing the resources of all the nodes connected.
In Grid Computing, each node independently manages its own resources.

Conclusion

In a Cluster computing network, the whole system works as a single unit. In contrast, each node in a Grid Computing network is independent and can be taken down or can be up at any time without impacting the functionality of other nodes.


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