Grid Computing: Types, Components and Applications

“The network is the Computer.”

- Scott McNealy

 

Data is a very vast concept and cannot be confined into a single entity. In order to analyze data properly, a scientist requires a huge amount of resources which is not available in a single computer. From another perspective, some tasks required by an organization cannot be performed independently and requires collaborations from other sources.

 

All these problems can be solved with the help of Grid Computing. You will learn more about Grid Computing in this blog.

 

Also Read | Cloud Computing vs Grid Computing

 

What is Grid Computing?

 

Grid Computing means setting up a network of computers so that they work together like a supercomputer. This helps to accomplish a joint task. Some tasks are too difficult and intensive and a single computer won't be able to handle it by itself. All the computers in the network work under the same protocol.

 

There are three types of machines in a typical Grid Computing network. They are given below.

 

1. Control node / server

 

A server that works as an administrator over the entire network and maintains a record for all the resources allocated to the network is called a control node.

 

2. Provider / grid node

 

A computer that contributes the resources allocated to it to the network is called a grid node.

 

3. User

 

The computer that uses all the resources in the network to complete the assigned task is called the user.

 

Grid Computing can be operated by running a special software on every computer connected to the grid network. All the tasks of the grid are coordinated and managed by the software. The main task is divided into sub tasks by the software and assigned to different computers in the grid. 

 

In this way, all the computers can work simultaneously on their assigned task. All the outputs of the computers are aggregated together to complete the main task. 

 

The software also allows the computers in the grid to communicate with one another and share information on the tasks given. Therefore, the tasks can be consolidated and combined together to form the main task.

 

All the computers participating in the network contribute resources to the grid like processing power, storage capacity, network bandwidth, etc, which gives the computers high computational power to perform the tasks. The computers in the grid network work together as a single entity.

 

Also Read | Best Supercomputers in the World

 

Components of Grid Computing

 

The Grid Computing environment is made up of primary grid components. There may be variance in the components with respect to different usage. The components may even combine together to form a hybrid component. The different components in a Grid Computing environment are given below.

 

1. User Interface

 

A Grid network provides an interface to its users that allows the user to launch applications using the resources provided in the grid. Portal style is incorporated in the interface so that all the queries of users are answered and various functions can be executed on the grid.

 

In a grid network, a large single virtual computer in the network that offers computing resources is viewed like an internet user who views content on the web.

 

2. Security

 

Security comprises one of the major factors of the Grid Computing environment. Authorization, authentication, data encryption, etc are some of the major security mechanics seen in a grid environment. 

 

An important feature is the Grid Security Infrastructure (GSI). GSI helps to establish tamper proof and secret communication between the different entities in a grid network.

 

OpenSSL is implemented to help with this task. It provides an important mechanism for users to perform tasks within the grid. It is known as a single sign on mechanism. This provides authorization and authentication mechanisms which helps to increase the security within the grid and protects the systems.

 

3. Scheduler

 

A scheduler is used to schedule tasks after identifying resources. A scheduler is not needed if the tasks are stand alone and no communication is necessary. It is needed when the tasks are interdependent and inter communication is required. The scheduler helps to coordinate different sub tasks so that they are executed properly.

 

There are different levels of schedulers in a grid environment. A high level scheduler assigns the tasks to different clusters and clusters have their own schedules who coordinate work on individual nodes.

 

4. Data Management

 

Data management makes use of a reliable and secure mechanism to move or make the data accessible to various nodes within the network. The Globus toolkit is an open source toolkit which can be used for data management.

 

A grid access to secondary storage (GASS) is offered by them which is a data management component. It utilizes GSI for user authorization and authentication and includes GridFTP which is built according to the standard FTP protocols. Movement of files is possible using GridFTP technology after authentication. Login process need not be applied at every node.

 

5. Workload and resource management

 

The actual completion of a task by utilizing a particular resource, checking of status and extraction of result after the task is completed is done by workload and resource component. 

 

If a user wants to launch an application on the grid then the application must be made aware of all the resources available in the grid which will be able to take up the workload.

 

Therefore, resource availability is determined by interacting with the workload manager and status is updated. This helps in effective workload and resource management in the grid environment.

 

Types of Grid Computing

 

Grid Computing is divided into several types depending upon the type of task and implementation technique. The main types of Grid Computing are given below.

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Types of Grid Computing

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1. Computational Grid Computing

 

Computational Grid Computing holds the largest share of Grid Computing usage in various industries. If a task is taking a longer time to execute then computational Grid Computing comes into play. The task is split into different subtasks and each subtask is executed by a separate node.

 

After completion of the tasks, the result of all the subtasks are merged together to achieve the main task. By splitting up the task, the result is achieved 'n' times faster, where 'n' represents the number of subtasks.

 

2. Data Grid Computing

 

In data Grid Computing, the grid is split onto multiple computers. The data can be placed in different networks or storage of computers. Even after splitting, the grid considers the data to be a single entity. This helps different users to access the data simultaneously and transfer or change the distributed data. 

 

A data grid can be considered to be a large data store and data is stored on the grid by all websites. The data is shared by the grid to all users. In this way, collaboration and knowledge transfer is possible between different users.

 

3. Collaborative Grid Computing

 

In collaborative Grid Computing, problems in a grid network are solved by seamless collaboration. Various technologies are used in this kind of computing to support the work between individuals. Accessing the work and supply of critical information on time can be carried out by the users. 

 

This helps to improve the overall productivity in the workforce and creativity is also encouraged, which benefits the organization. Geographical barriers can be overcome and remote individuals are able to work together, which helps to enhance the work experience.

 

4. Manuscript Grid Computing

 

In manuscript Grid Computing, handling large amounts of texts and images becomes easier. Continuous accumulation of text and image blocks can be carried out while simultaneously performing operations on previous batches of text and image blocks. It can be carried out by a simple framework of Grid Computing where images and manuscripts are processed in parallel.

 

5. Modular Grid Computing

 

In modular Grid Computing, computer resources are disaggregated in a system. The resources may include GPUs, networking, storage and memory. Then the computing resources and required assets are combined by IT teams which are used to support specific services or applications. 

 

The resources are combined with specific softwares for different applications. When the applications expire, then the computing support is withdrawn and resources are used to support other applications. Original equipment manufacturers (OEMs) create application specific modular grids which are essential in modular Grid Computing.

 

Also Read | Introduction to Mobile Computing

 

Applications of Grid Computing

 

Grid Computing technology is used in various fields like science, health, entertainment and business. It is an up and coming technology which is still in its initial phase and garnering lots of support with every passing day. The main applications of Grid Computing are given below.

 

1. Life science

 

One of the fastest growing application areas of grid technology is life science. Different disciplines in life sciences like computational biology, genomics, bioinformatics, neuroscience, etc are beginning to use Grid Computing. 

 

Accessing, collection and mining of relevant data can be done by medical practitioners. Large scale simulations and analyzing and connection of remote instruments to medical infrastructure can be done by medical staff with the help of Grid Computing.

 

2. Engineering

 

Engineering applications are very resource intensive and have high costs. Grid Computing has contributed a significant amount to engineering applications and has lowered the costs to better suit the organizations' needs. 

 

Engineering services that require intensive testing facilities and collaborative design efforts have started using Grid Computing technology. Automotive and aerospace are some of the leading industries that have adopted this technology. Grid Computing also helps to speed up time consuming engineering procedures.

 

3. Data oriented application

 

In today's world, lots of data is being generated from different sources like smart gadgets, scientific instruments, sensors and IoT devices. With this explosion of data, Grid Computing has a very crucial role. 

 

Grids are used in collecting, storing and analyzing data. They are also used to interpret the data and observe patterns in the stored data to synthesize knowledge.

 

4. e-Science

 

Research collaborations require a huge amount of storage space as it generates around petabytes of data. The organizations that are involved in this research have adopted Grid Computing to help them store this data. Advanced computational resources are also needed by them to perform data intensive processing. 

 

A resource sharing mechanism is provided by grid computing that shares computing capabilities to a single virtual organization. The virtual supercomputer helps to facilitate the high demand for sharing of resources and a secure framework is integrated to help in easy exchange and access of data.

 

5. Commercial Application

 

There are many commercial applications of Grid Computing too. The online entertainment and gaming industry requires computation intensive resources like computers and storage networks. 

 

Based on the computing requirements of the grid environment in the gaming or entertainment industry, the resources are selected. The number of participating players and traffic volume are some of the important aspects which are considered. 

 

The upfront costs of software and hardware resources are reduced and collaborative gaming is promoted. Grid Computing can be used to enhance the video appearance by adding special effects in motion pictures. 

 

It also helps in theater film production by processing different portions concurrently, which results in less production time.

 

Also Read | Latest Trends in Cloud Computing

 

With the evolution of Grid Computing, industries are learning how to embrace it by accelerating business processes, building versatile networks and aiming for holistic success. 

 

A Grid Computing system can be both simple and complex. A simple system can consist of similar computers utilizing the same operating system. A complex system can consist of inter networked systems comprising all computer platforms.