Grid computing is characterized as a distributed architecture of numerous computers connected by networks and working together to complete a single activity. Computers work together in a data grid to coordinate the tasks at hand in this system. But how? Continue reading and learn its types, components, and everything you need to know about it.
The provision of cloud computing basics is frequently (but not always) linked to grid computing. Although the pros and cons of cloud computing are still being discussed, cloud computing jobs are hot and on the rise, and grid computing has come to the fore. So, what is it?
Table of Contents
What is grid computing?
As a virtual supercomputer, grid computing is a network of connected computers to accomplish massive tasks like weather modeling or data analysis.
You can create and use massive computer grids over the cloud, paying just for what you use if necessary to avoid the effort and cost of obtaining and setting up the required resources yourself. Additionally, the processing time is greatly decreased by dividing jobs among several processors, increasing efficiency and reducing resource wastage.
It is a subtype of distributed computing, where a virtual supercomputer is made up of machines connected by a bus, typically Ethernet or occasionally the Internet, across a network.
It is also possible to think of it as a type of parallel computing where, instead of having numerous CPU cores on a single machine, the cores are distributed among several machines.
Although the idea is not new, it has not yet reached its full potential because no approved standards for rules and protocols have been established.
Who invented grid computing?
Early in the 1990s, Steve Tuecke, Ian Foster, and Carl Kesselman developed the concept of grid computing. They created the Globus Toolkit standard, which had grids for controlling data processing, data storage, and heavy computation.
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Characteristics of grid computing
Grid computing has introduced its own unique traits while including several distributed systems and parallel systems’ properties. So, what are the features of it?
- Large scale: Resources for grid computing are dispersed over numerous places and are connected through a network. Resources are scalable and include memory and storage.
- Geographical distribution: Due to the geographical diversity of the grid nodes, fault tolerance is supported.
- Heterogeneity and loosely coupled: Grid machines are heterogeneous in nature, which implies that no two machines in a network will have the same platform, operating system, or architecture.
- Resource sharing: The grid allows organizations to share resources among grid machines.
- Multiple administrations: Administrators can control resources in the grid machine based on the data and processes of each organization.
- Resource coordination: To accomplish shared objectives, each resource machine in grids collaborates with the host.
- Quality of Service (QoS): Grid uses many machine resources to give customers high-quality services at the desired degree of performance.
- Transparent access: Despite the grid’s complexity, clients can only perceive it as a single virtual computer operating at peak efficiency.
What are the components of grid computing architecture?
With the help of several computers working together to pool resources, grid computing is a distributed architecture.
A set of fundamental grid components make up a grid computing environment like:
- User interface
- Data management
- Workload & resource management
Specific components may or may not always be a part of the grid network because grid designs and their anticipated usage vary. In particular situations, these components can be merged to create a hybrid component. Although the arrangement of the parts may vary based on the use case, being aware of their functions will be useful when creating grid-enabled applications. So, let’s learn them.
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Users can examine a range of information using a single interface they offer. Similarly, a grid portal provides a user interface that enables users to run programs using resources made available by the grid.
The interface includes a portal-style design to facilitate effective grid querying and execution. Likewise to how an internet user sees a single, huge virtual computer giving computational resources, a grid user sees a single, unified instance of content on the web.
One of the main issues with grid computing environments is security. Data encryption, authentication, and authorization are only a few examples of security measures. The grid security infrastructure (GSI) is a crucial component in this. It describes requirements for establishing secure communication between software entities via a grid network.
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It incorporates OpenSSL implementation and gives users a single sign-on option so they can operate the grid. It offers strong security by offering means for system protection, such as authentication and authorization.
Scheduling the tasks to run on the resources that have been identified is the next step. If standalone tasks without interdependencies are to be carried out, a scheduler might not be required. However, the job scheduler would be sufficient to coordinate the execution of many subtasks if you wanted to run specific tasks concurrently that need inter-process communication.
Additionally, schedulers at various levels work in a grid context. For instance, a cluster might stand in for an autonomous resource, managing the nodes it contains using its own scheduler. Consequently, a high-level scheduler might occasionally be needed to complete a task on the cluster. However, the cluster uses a separate scheduler to manage work on its individual nodes.
Grid environments require effective data management. A secure and trustworthy technique is required to relocate or open any data or application module to different grid nodes.
Workload & resource management
The workload and resource component allows for the actual launch of a task on a certain resource, monitors its status, and receives the results after the job has finished. Consider a scenario where a user wants to run a grid application. If so, the application needs to be aware of the grid’s resources that are ready to take on the task.
As a result, it communicates with the workload manager to learn about the resource availability and updates the status as necessary. Thanks to this, different nodes on the grid gert aid in effective workload and resource management.
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Grid computing types
What is grid computing types? Various types of grid computing exist per their applications and the project at hand:
- Computational grid computing
- Data grid computing
- Collaborative grid computing
- Manuscript grid computing
- Modular grid computing
Let’s take a closer look at them to understand better.
Computational grid computing
Today, computational grids make up the greatest portion of grid computing utilization across industries, and this trend is anticipated to continue in the future.
A computational grid enters the picture when a task takes longer to complete than anticipated. In this instance, the primary job is divided into several subtasks, and each subtask is carried out concurrently on a different node. After each subtask is finished, the findings are merged to produce the outcome of the main task. The task is divided, and when a separate machine executes each subtask, the final result is obtained O(n) times faster (where “n” represents the number of subtasks).
There are numerous real-world situations where computational grids are useful. For one corporation with an online marketplace, a computational grid might speed up the production of business reports. Customers value their time; thus, the business can use computational grids to produce reports in seconds rather than minutes. Such grids significantly outperform conventional systems in terms of performance.
Data grid computing
Grids that spread data over numerous computers are referred to as data grids.
Data grids allow for data distribution across a network of computers or storage, similar to computational grids where operations are separated. Despite the separation, the grid practically treats them as a single entity.
Data grid computing enables several users to view, modify, or transmit scattered data simultaneously.
A data grid, for example, might be utilized as a huge data storage where each website maintains its own data. In this situation, the grid allows all grid users to share data in concert. Such a grid enables enhanced knowledge transmission and collaboration among grid users.
Collaborative grid computing
By enabling seamless collaboration, collaborative grid computing provides solutions to issues. Different technologies are used in this form of computing to promote collaboration amongst people.
Individual employees’ ability to quickly access each other’s work and important information boost workforce innovation and productivity thanks to collaborative grid computing, which significantly impacts enterprises.
Collaborative grid computing enables remote workers to collaborate, removes barriers based on distance, and adds features that improve the working experience. For instance, a collaborative grid lets users access and work on text-based documents, graphics, design files, and other job-related items simultaneously.
Manuscript grid computing
Manuscript grid computing is highly useful when organizing many images and text blocks. While processing and performing actions on previous block batches, this grid pattern permits the continual accumulation of image and text blocks.
It uses a straightforward grid computing structure to simultaneously process huge amounts of text, manuscripts, and photos.
Modular grid computing
In a system or chassis, computing resources like storage, GPUs, memory, and networking can be disaggregated through a process known as modular grid computing. IT teams can incorporate the necessary resources and computational power to support particular apps or services.
In a modular grid, a collection of resources and software are fundamentally combined for various purposes. For instance, drives for the CPU and GPU might be housed in a server rack chassis. They can be linked together to form a server configuration ideal for a certain application using an additional high-speed and low-latency fabric.
A set of computer resources and services are defined to assist apps when they are created. Once the programs have run their course, computing support is discontinued, and resources are released, making them available for new apps.
In actuality, original equipment manufacturers (OEMs) play a significant role in modular grid computing since their cooperation is essential in developing application-specific modular grids.
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Grid computing advantages and disadvantages
Grid computing has some advantages and disadvantages that are explained below.
Grid computing advantages
These are the advantages of grid computing:
- Easy collaboration
- Cheaper servers
- Efficient hardware usage
- In less time, it can resolve bigger issues.
- No need for physical presence
Grid computing disadvantages
What are the disadvantages of grid computing? Like everything else in the world, grid computing has some disadvantages too.
- Still a developing area
- Need for large SMP
- Requiring fast interconnect
- The unwillingness to share resources
Real-life grid computing examples
Scientific research has greatly benefited from grid computing, which enables scientists worldwide to evaluate and store enormous amounts of data. These are some examples of scientific projects that utilized grid computing.
The MCell project investigates cellular microphysiology by simulating and researching molecular interactions both inside and outside of cells using advanced “Monte Carlo” diffusion and chemical reaction algorithms.
Grid technologies have made it possible to deploy different MCell modules on a broad scale since MCell can now run biochemical simulations on a variety of resources, such as clusters and supercomputers.
Large-scale engineering-focused grid applications have been implemented in the United States by NASA Information Power Grid (NASA IPG).
IPG is NASA’s distributed computing grid, which includes everything from personal computers to sizable databases and research equipment. Complete airplane design is one use that NASA is very interested in. Each important component of an aircraft, including the airframe, wing, stabilizer, engine, landing gear, and human aspects, is managed by a different technical team that is frequently geographically dispersed. A grid that uses concurrent engineering to coordinate tasks unites the work of all the teams.
Grid computing also expedites the processes required in creating apps with an engineering focus in this way.
The University of California, Berkeley created BOINC, which enables you to use your home computer to donate processing power to research projects in various scientific fields. More are included at the end of the article; BOINC is frequently used by academic projects looking for volunteers from the general population.
Before serving as a valuable foundation for various distributed applications in fields as varied as mathematics, medicine, molecular biology, climatology, and astronomy, BOINC was initially created to support the SETI@home project.
GIMPS – Great Internet Mersenne Prime Search
To find new Mersenne primes with record-breaking sizes, the Great Internet Mersenne Prime Search (GIMPS) was established in January 1996. To find these “needles in a haystack,” GIMPS uses the computing power of thousands of personal computers.
The goal of World Community Grid, which is supported and run by IBM, is to establish the biggest public computing grid that benefits humanity.
World Community Grid’s research initiatives have examined various parts of the human genome, HIV, dengue, muscular dystrophy, and cancer using the idle time of computers all across the world.
Grid computing applications/use cases
Grid infrastructure will develop to keep up with the rate of change and offer solid platforms as companies strive to optimize their IT infrastructure to better exploit the true potential of grids.
Who uses grid computing? These are the most common applications of grid computing:
- Life science
- Data-oriented applications
- Commercial applications
How grid computing effect this fields?
One of the grid computing application fields with the quickest growth is life science. The grid technology has been quickly adopted by a number of life science fields, including computational biology, bioinformatics, genomics, neurology, and others.
Medical professionals can efficiently access, gather, and mine pertinent data. The grid also enables medical professionals to link distant devices to already-existing medical infrastructure and conduct extensive simulations and analyses.
Grid technologies have been chosen by a number of engineering businesses that demand collaborative design efforts and data-intensive testing facilities, such the automotive or aerospace industries.
The cost of engineering applications that require a lot of resources has been greatly decreased because to grid computing.
Grids are essential in light of the growth in data. Grids are being used to gather, store, and analyze data as well as to infer patterns from that data to create knowledge.
Universities and institutions involved in advanced research partnership initiatives must examine and interpret a vast amount of data. Data analysis for high-energy physics experiments, DNA sequence analysis in COVID-19-like scenarios, and the creation of earth system models (ESM) using information gathered from various remote sensing sources are a few examples of these undertakings.
Grid computing offers a single virtual organization that shares computing resources, acting as a vehicle for resource sharing. The virtual supercomputer makes it possible to share resources on demand and incorporates a secure framework for simple data access and exchange.
Grid computing supports a wide range of commercial applications, including those in the online gaming and entertainment sectors, which depend heavily on computationally intensive resources like computers and storage networks. In a grid setup for gaming, the resources are chosen based on computing requirements. It takes into account variables like the volume of traffic and the quantity of gamers.
Such grids encourage group gaming and lower the initial expenditure for hardware and software resources in games that are driven by demand. Grid computing also improves the aesthetic appeal of the movie by including special effects in the media sector. The grid also aids in the making of theater films since various parts can be processed simultaneously, requiring less production time.
Is it sounds like cloud computing? But it is not. Let’s see the differences between grid computing and cloud computing.
Comprasion: Grid computing vs cloud computing
Let’s examine how cloud computing and grid computing vary from one another.
|Cloud computing||Grid computing|
|The client-server computing architecture is used in cloud computing.||A distributed computer architecture is used in grid computing.|
|High scalability.||Normal scalability.|
|Compared to grid computing, cloud computing is more adaptable.||In comparison to cloud computing, grid computing is less versatile.|
|Centralized management.||Decentralized management.|
|Infrastructure providers own cloud servers in cloud computing.||Grids are owned and managed by the organization in grid computing.|
|IaaS, PaaS, and SaaS services are used in cloud computing.||Systems like distributed computing, distributed information, and distributed ubiquitous are used in grid computing.|
|It can be accessed via common web protocols.||It can be accessed via grid middleware.|
Despite having many similarities, cloud computing and grid computing frequently serve quite distinct purposes, projects, and use cases. While Grid Computing, when utilized properly, makes using physical hardware more affordable, Cloud computing is wonderful for flexibility, ease-of-use, and security.
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What about the diffences between grid computing and distributed computing? Don’t worry, we explained it too.
Comparison: Grid computing vs distributed computing
Organizations employ distributed computing and grid computing to build intricate networks that can quickly handle resource-intensive operations. They do, however, differ significantly in certain key ways, as explained below.
- The term “distributed computing” typically refers to the management or pooling of hundreds or thousands of computer systems, each of which has a lower memory and processing capacity. Grid computing, on the other hand, has certain unique traits. It focuses on the effective management of workloads while leveraging a pool of heterogeneous systems that includes all of an enterprise’s computational resources (servers, networks, storage, and information) working together to build one or more sizable computing resource pools. Grid computing has no restrictions on users, departments, or origins.
- Grid computing differs from conventional distributed computing in that it focuses on the capacity to facilitate computation across various administrative domains. Grids provide a method for virtualizing computer resources to use information technology resources inside an organization in the best possible way. It can be spread over a local, metropolitan, or wide-area network due to its notion of support for numerous administrative policies and security authentication and authorisation processes.
Is grid computing expensive?
According to various reaseachs, they came to the conclusion that grids are significantly cheaper than typical computer centers, with expenses ranging from $0.20 to $0.94 per CPU and hour as opposed to $2 to $12 per CPU and hour.
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Several industry sectors, including IT, automotive, aerospace, astronomy, the physical and life sciences, and even the media sector, have benefited from grid computing. Regardless of how widely spaced out the network nodes are geographically, grid computing enables enterprises to execute tasks significantly more quickly.
As grid computing develops, an increasing number of companies must decide how to best adopt it by creating flexible networks, speeding up their business operations, and creating comprehensive profitability.