What Is Grid Computing? Key Components, Types, and Applications
Grid computing is a distributed architecture that uses a group of computers to combine resources and work together.
Grid computing is defined as a distributed architecture of multiple computers connected by networks that work together to accomplish a joint task. This system operates on a data grid where computers interact to coordinate jobs at hand. This article explains the fundamentals of grid computing in detail.
Table of Contents
What Is Grid Computing?
Grid computing is a distributed architecture of multiple computers connected by networks to accomplish a joint task. These tasks are compute-intensive and difficult for a single machine to handle. Several machines on a network collaborate under a common protocol and work as a single virtual supercomputer to get complex tasks done. This offers powerful virtualization by creating a single system image that grants users and applications seamless access to IT capabilities.
How Grid Computing Works
A typical grid computing network consists of three machine types:
- Control node/server: A control node is a server or a group of servers that administers the entire network and maintains the record for resources in a network pool.
- Provider/grid node: A provider or grid node is a computer that contributes its resources to the network resource pool.
- User: A user refers to the computer that uses the resources on the network to complete the task.
Grid computing operates by running specialized software on every computer involved in the grid network. The software coordinates and manages all the tasks of the grid. Fundamentally, the software segregates the main task into subtasks and assigns the subtasks to each computer. This allows all the computers to work simultaneously on their respective subtasks. Upon completion of the subtasks, the outputs of all computers are aggregated to complete the larger main task.
The software allows computers to communicate and share information on the portion of the subtasks being carried out. As a result, the computers can consolidate and deliver a combined output for the assigned main task.
Grid computing can be viewed as a subset of distributed computing, where a virtual supercomputer integrates the resources of several independent computers that are distributed across geographies. Computers participating in a grid contribute resources such as processing power, network bandwidth, and storage capacity to perform operations requiring high computational power. The overall grid architecture looks like a single computing entity.
In grid computing, each computing task is broken into small fragments and distributed across computing nodes for efficient execution. Each fragment is processed in parallel, and, as a result, a complex task is accomplished in less time. Let’s consider this equation:
X = (4 x 7) + (3 x 9) + (2 x 5)
Typically, on a desktop computer, the steps needed here to calculate the value of X may look like this:
- Step 1: X = 28 + (3 x 9) + (2 x 5)
- Step 2: X = 28 + 27 + (2 x 5)
- Step 3: X = 28 + 27 + 10
- Step 4: X = 65
However, in a grid computing setup, the steps are different as three processors or computers calculate different pieces of the equation separately and combine them later. The steps look like this:
- Step 1: X = 28 + 27 + 10
- Step 2: X = 65
As seen above, grid computing combines the involved steps due to the multiplicity of available resources. This implies fewer steps and shorter timeframes.
Grid computing is enabled via an open set of standards and protocols such as open grid services architecture (OGSA) that allow communication across heterogeneous systems and environments that are geographically dispersed. With grid computing, organizations can pool resources and computing for high-weighted tasks or share them across networks to allow collaboration. Enterprises can thus optimize computing and resources perfectly irrespective of their locations.
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Key Components of Grid Computing
A grid computing environment consists of a set of primary grid components. As grid designs and their expected usage vary, specific components may or may not always be a part of the grid network. These components can be combined to form a hybrid component in specific scenarios. Although the combination of elements may differ depending on use cases, understanding their roles can help you while developing grid-enabled applications.
Let’s understand the key components of a grid computing environment.
Grid Computing: Key Components
1. User interface
Today, users are well-versed with web portals. They provide a single interface that allows users to view a wide variety of information. Similarly, a grid portal offers an interface that enables users to launch applications with resources provided by the grid.
The interface has a portal style to help users query and execute various functions on the grid effectively. A grid user views a single, large virtual computer offering computing resources, similar to an internet user who views a unified instance of content on the web.
2. Security
Security is one of the major concerns for grid computing environments. Security mechanisms can include authentication, authorization, data encryption, and others. Grid security infrastructure (GSI) is an important ingredient here. It outlines specifications that establish secret and tamper-proof communication between software entities operating in a grid network.
It includes OpenSSL implementation and provides a single sign-on mechanism for users to perform actions within the grid. It offers robust security by providing authentication and authorization mechanisms for system protection.
3. Scheduler
On identifying the resources, the next step is to schedule the tasks to run on them. A scheduler may not be needed if standalone tasks are to be executed that do not showcase interdependencies. However, if you want to run specific tasks concurrently that require inter-process communication, the job scheduler would suffice to coordinate the execution of different subtasks.
Moreover, schedulers of different levels operate in a grid environment. For example, a cluster may represent an independent resource with its own scheduler to manage the nodes it contains. Hence, a high-level scheduler may sometimes be required to accomplish the task done on the cluster, while the cluster employs its own separate scheduler to handle work on its individual nodes.
4. Data management
Data management is crucial for grid environments. A secure and reliable mechanism to move or make any data or application module accessible to various nodes within the grid is necessary. Consider the Globus toolkit — an open-source toolkit for grid computing.
It offers a data management component called grid access to secondary storage (GASS). It includes GridFTP built on the standard FTP protocol and utilizes GSI for user authentication and authorization. After authentication, the user can move files using the GridFTP facility without going through the login process at every node.
5. Workload & resource management
The workload & resource component enables the actual launch of a job on a particular resource, checks its status, and retrieves the results when the job is complete. Say a user wants to execute an application on the grid. In that case, the application should be aware of the available resources on the grid to take up the workload.
So, it interacts with the workload manager to determine the resource availability and updates the status accordingly. This helps in efficient workload and resource management for various nodes on the grid.
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Types of Grid Computing With Examples
Grid computing is divided into several types based on its uses and the task at hand. Let’s understand the types of grid computing with some examples.
Grid Computing Types
1. Computational grid computing
Computational grids account for the largest share of grid computing usage across industries today, and the trend is expected to stay the same over the years to come. A computational grid comes into the picture when you have a task taking longer to execute than expected. In this case, the main task is split into multiple subtasks, and each subtask is executed in parallel on a separate node. Upon completion, the results of the subtasks are combined to get the main task’s result. By splitting the task, the end result is achieved O(n) times faster (where ‘n’ denotes the number of subtasks) than when a single machine executes the task.
Computational grids find application in several real-life scenarios. For example, a computational grid can speed up the business report generation for a company with an online marketplace. As time is an important factor for customers, the company can use computational grids to generate reports within seconds rather than minutes. Such grids result in substantial performance improvement compared to traditional systems.
2. Data grid computing
Data grids refer to grids that split data onto multiple computers. Like computational grids where computations are split, data grids enable placing data onto a network of computers or storage. However, the grid virtually treats them as one despite the splitting. Data grid computing allows several users to simultaneously access, change, or transfer distributed data.
For instance, a data grid can be used as a large data store where each website stores its own data on the grid. Here, the grid enables coordinated data sharing across all grid users. Such a grid allows collaboration along with increased knowledge transfer between grid users.
3. Collaborative grid computing
Collaborative grid computing solves problems by offering seamless collaboration. This type of computing uses various technologies that support work between individuals. As individual workers can readily access each other’s work and critical information on time, it improves overall workforce productivity and creativity, which benefits organizations massively.
It overcomes geographical barriers and adds capabilities that enhance work experience by allowing remote individuals to work together. For example, with a collaborative grid, all users can access and simultaneously work on text-based documents, graphics, design files, and other work-related products.
4. Manuscript grid computing
Manuscript grid computing comes in handy when managing large volumes of image and text blocks. This grid type allows the continuous accumulation of image and text blocks while it processes and performs operations on previous block batches. It is a simple grid computing framework where vast volumes of text or manuscripts and images are processed in parallel.
5. Modular grid computing
Modular grid computing relates to disaggregating computing resources in a system or chassis, where resources can include storage, GPUs, memory, and networking. IT teams can then combine the required assets and computing resources to support specific apps or services.
Fundamentally, in a modular grid, a set of resources is combined with software for distinct applications. For example, CPU and GPU drives may reside in a server rack chassis. They can be interconnected with an auxiliary high-speed and low-latency fabric to create a server configuration that is optimized for a particular application.
When applications are created, a set of computing resources and services are defined to support them. Subsequently, when the applications expire, computing support is withdrawn, and resources are set free, making them available for other apps. Practically, original equipment manufacturers (OEMs) play a key role in modular grid computing as their cooperation is critical in creating modular grids that are application-specific.
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Top 5 Applications of Grid Computing
Grid computing acts as an enabling technology for developing several applications across diverse fields like science, business, health, and entertainment. According to Wipro’s 2021 report, cloud leaders expect a 29% increase in the usage of grid computing as a complementary technology to boost cloud ROI by 2023.
As industries continue to streamline their IT infrastructure to better realize the true potential of grids, grid infrastructure will evolve to match the pace of change and provide stable platforms. Here are the top five applications of grid computing.
Grid Computing Applications
1. Life science
Life science is one of the fastest-growing application areas of grid computing. Various life science disciplines such as computational biology, bioinformatics, genomics, neuroscience, and others have embraced grid technology rapidly. Medical practitioners can access, collect, and mine relevant data effectively. The grid also enables medical staff to perform large-scale simulations and analyses and connect remote instruments to existing medical infrastructure.
For example, the MCell project explores cellular microphysiology using sophisticated ‘Monte Carlo’ diffusion and chemical reaction algorithms to simulate and study molecular interactions inside and outside cells. Grid technologies have enabled the large-scale deployment of various MCell modules, as MCell now runs on a large pool of resources, including clusters and supercomputers, to perform biochemical simulations.
2. Engineering-oriented applications
Grid computing has contributed significantly to reducing the cost of resource-intensive engineering applications. Several engineering services which require collaborative design efforts and data-intensive testing facilities like the automotive or aerospace industries have opted for grid technologies.
NASA Information Power Grid (NASA IPG) has deployed large-scale engineering-oriented grid applications in the U.S. IPG is NASA’s computational grid with distributed computing resources — from computers to large databases and scientific instruments. One application that is of great interest to NASA is complete aircraft design. A separate, often geographically distributed engineering team manages each key aspect of an aircraft, such as the airframe, wing, stabilizer, engine, landing gear, and human factors. The work of all the teams is integrated by a grid that employs concurrent engineering for coordinating tasks.
In this way, grid computing also speeds up procedures involved in developing engineering-oriented applications.
3. Data-oriented applications
Today, data is emerging from every corner — from sensors, smart gadgets, and scientific instruments to many new IoT devices. With the explosion of data, grids have a crucial role to play. Grids are being used to collect, store, and analyze data, and at the same time, derive patterns to synthesize knowledge from that same data.
Distributed aircraft maintenance environment (DAME) is a fit use case of a data-oriented application. DAME is a grid-based distributed diagnostic system for aircraft engines developed in the U.K. It uses grid technology to manage large volumes of in-flight data collected by operational aircraft. The data is used to design and develop a decision support system for the diagnosis and maintenance of aircraft by utilizing geographically distributed resources and data that are combined under a virtual framework.
4. Scientific research collaboration (e-Science)
Universities and institutions participating in advanced research collaboration programs have an enormous amount of data to analyze and process. Some examples of these projects include data analysis work for high-energy physics experiments, genome sequence analysis in COVID-19-like scenarios, and the development of earth system models (ESM) by collecting data from several remote sensing sources.
Organizations involved in research collaboration require substantial storage space as they regularly generate petabytes of data. They also need advanced computational resources to perform data-intensive processing.
In this case, grid computing provides a resource-sharing mechanism by offering a single virtual organization that shares computing capabilities. The virtual supercomputer facilitates the on-demand sharing of resources and integrates a secure framework for easy data access and interchange.
5. Commercial applications
Grid computing supports various commercial applications such as the online gaming and entertainment industry, where computation-intensive resources, such as computers and storage networks, are essential. The resources are selected based on computing requirements in a gaming grid environment. It considers aspects such as the volume of traffic and the number of participating players.
Such grids promote collaborative gaming and reduce the upfront cost of hardware and software resources in on-demand-driven games. Moreover, in the media industry, grid computing enhances the visual appearance of the motion picture by adding special effects. The grid also helps theater film production as different portions are processed concurrently, requiring less production time.
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Takeaway
Grid computing has proved beneficial for various industry verticals, such as IT, automotive, aerospace, astronomy, physical sciences, life science, and even the media industry. By distributing work across the network nodes with grid computing, organizations can complete tasks much faster, irrespective of the geographic spread of the nodes.
As grid computing evolves, more and more industries need to ascertain how best to embrace it by developing versatile networks, accelerating their business processes, and building holistic success.
Do you think grid technology is essential for modernizing today’s cloud-enabled world? Comment below or let us know on LinkedIn, Twitter, or Facebook. We’d love to hear from you!
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