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Grid computing is a type of distributed computing that brings together various compute resources located in different places to accomplish a common task.
Both types of computing rely on shared computing infrastructure, but grid computing focuses more on solving large scale scientific or engineering problems while distributed computing focuses on simpler tasks.
Grid computing is often associated with a type of computing known as a “grand challenge”—a computing problem grounded in science or engineering that has broad applications. Perhaps the best known grand challenge that grid computing has helped power is the large Hadron Collider at CERN, the world’s most powerful particle accelerator.
In addition to tackling grand challenges, grid computing is also used for a variety of more practical business purposes, including big data management and high-speed data analytics, insight generation, scientific research, complex weather and financial simulations and high performance computing (HPC).
Cloud computing, the on-demand access of computing resources over the internet, is similar to grid computing but different in several important ways. While cloud computing and grid computing infrastructures are both considered distributed systems, cloud computing is based on a centrally managed, highly flexible client-server model that is easy for users to access. In the cloud, businesses access services over the internet using standard protocols and only pay for the compute resources they need.
Grid computing, on the other hand, relies on a collaboratively managed infrastructure where resources are owned and managed by a single organization. This makes it optimal for enterprises running consistent workloads but makes it more difficult to access and less scalable than cloud computing architectures. Rather than using standard computing protocols to access a particular service, for example, grid users must use grid middleware, specialized applications for grid computing architectures.
A grid computing environment is made up of different nodes, which are computers, devices and resources programmed to perform a specific task. This aspect of grid computing makes it more diverse than cluster computing, another type of computing where compute resources are shared over a network. While computer clusters have fixed hardware and tasks, grid computing has a far more flexible resource sharing environment.
Typically, a grid computing network comprises two kinds of components: nodes and middleware.
Grid computing architectures depend upon three types of grid nodes to complete a grid computing task:
In a grid computing infrastructure, middleware is known as grid middleware and functions as a software layer that enables the various nodes to communicate and exchange resources. Grid middleware is responsible for coordinating the requests facilitated by user nodes with the available resources kept by the provider nodes.
Grid middleware is highly specialized and capable of handling requests for a wide range of compute resources such as processing power (CPU), memory and storage. It is critical to the functionality of the grid infrastructure, balancing resources to prevent misuse and ensuring the grid computing system runs safely and efficiently.
A typical grid computing architecture is made up of four layers that consist of applications, middleware, resources, and a bottom layer that allows each node to connect to a network:
Grid computing is typically broken down into five fundamental types based on purpose.
Computational grids are the most common type of grid computing infrastructure, deployed for a wide range of high performance computing (HPC) tasks. Computational grid computing is highly resource-intensive, combining the computing power of multiple high-performance computers to perform complex simulations and solve large-scale mathematical problems and algorithms.
A computational grid can divide a complex task into smaller, simpler subtasks and assign each one to a node. This process, known as parallel computing or parallel programming, dramatically reduces the time and cost of solving complex, resource-intensive problems that are integral to cutting edge technologies like artificial intelligence (AI), machine learning (ML) and blockchain. Because of its speed, parallel computation is ideal to advanced technologies that require real-time processing like self-driving cars, weather modeling and Internet of Things (IoT) applications.
A scavenging grid, also known as a CPU scavenging grid or a scavenging cycle, has a similar layout and purpose to a computational grid but with one key difference. On a scavenging grid, nodes and computers only contribute available resources to the larger grid. The term scavenging in this context refers to the process of searching a grid of connected computing resources for availability.
On a scavenging grid, some nodes are performing tasks related to the grid’s larger purpose while others are being used for other, unrelated purposes. If network users need to access computers for non-grid-related purposes, the grid’s software simply identifies the free nodes and compute resources available and allocates them.
Data grids are large grid-computing networks that connect computers to increase data storage capacity. Data grid computing breaks down a large data set so it can be stored on multiple computers connected over a network. Computers on a data grid typically exchange data and resources across a large geographical area, connecting users in remote locations.
Data grids are ideal for compute tasks that can be broken down into smaller subtasks and solved in parallel. They are widely used in microservices technologies and as the basis for private clouds where devices are pooled together and a subset of their resources is allocated to a specific purpose. Furthermore, virtual machines (VMs) are often featured in a data grid, allowing for more efficient resource pooling for common compute tasks like data processing and storage.
Collaborative grids, or collaborative grid-computing frameworks, allow groups of individuals to leverage a computational grid to more easily access shared work and resources.
Collaborative grids allow for widely dispersed teams to share expertise and contribute work in real time as they pursue a common goal. For example, collaborative grids enable the work of many climate scientists and physicists working to solve problems via shared data and computational resources from different universities and institutions around the globe.
Modular grids are focused on separating the compute resources within a specific system into separate modules to increase application performance. In a modular grid, commonly shared resources like GPUs, storage and memory are broken down and recombined for more efficiency in the running of specific applications and services.
The modular approach enables IT teams to be more flexible when customizing a compute environment to suit their needs. For example, in a modular grid, the configuration can be tailored to meet the specific resource needs of an individual application or service.
Grid computing allows enterprises to process large volumes of data faster and more efficiently than in a more traditional setup. Enterprises leveraging grid computing have achieved more flexibility, scalability and cost effectiveness by utilizing resources for a number of business purposes. Here are some of the most common benefits organizations have realized from grid computing.
Grid computing allows large organizations to handle enormous, complex tasks more efficiently by breaking them down into smaller subtasks. Once broken into separate, smaller problems, grid computing uses the compute resources of connected nodes to solve the problems in parallel, saving time and energy.
In a grid computing environment, compute resources can be added or subtracted on an on-demand basis, reducing price and optimizing compute compute resources. This is particularly useful with workloads where demands fluctuate greatly and enterprises need to dynamically scale, adding and removing on an as needed basis.
Grid computing helps organizations save money by extracting the most out of their existing hardware. The grid computing framework allows enterprises to reuse existing computers, optimizing resources like memory, storage, GPUs and more that would otherwise be sitting unused.
Grid computing environments are highly flexible since they function across a grid computing network of interconnected nodes that don’t have to be in the same physical location. Scientists and researchers at universities around the globe use a grid computing environment to tackle complex, data-rich problems like climate change and meteorological patterns using the same supercomputing resources.
Large enterprises often have compute resources that sit idle or underused but still require maintenance and consume energy. Grid computing allows these enterprises to distribute workloads across underutilized resources, increasing infrastructure optimization. And unlike other types of computing environments, grid computing frameworks don’t require the latest, most technologically advanced tools but can run on existing hardware.
Grid computing is widely used by large organizations across a wide range of industries. Universities have deployed grids to solve large, complex problems that involve supercomputers and collaboration with colleagues around the globe. Powerful virtual supercomputers relying on a grid-computing framework have tackled complex scientific and engineering tasks related to climate change, astrophysics and more. Here are some of the most common grid computing use cases.
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1. Grid Computing Market Size, Future scope & Growth report by 2031, Straits Research, October 2023
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