Distributed computing and grid computing are defined as solutions that leverage the power of multiple computers to run as a single, powerful system. However, they differ in application, architecture, and scope. This article highlights the key comparisons between these two computing systems.
Table of Contents
Distributed computing systems consist of several software components spread over multiple computers. However, this setup operates as a single system. Distributed computing has a broad scope and can describe systems in close physical proximity (perhaps connected over a local network) or geographically distant (linked over a vast area network).
Distributed computing systems are made up of numerous configurations, including, but not limited to, personal computers, workstations, minicomputers, and mainframes. Distributed computing combines the capabilities of these configurations to make the entire network operate as a single, powerful computing system. In a distributed computing model, the hardware and software components are shared across all the computers connected to the network to enhance performance and efficiency.
A narrow definition of distributed computing limits its scope to components and programs shared over computing systems connected over a local area network. However, broader definitions describe such a system as one that includes tasks and programs shared among multiple computers, either in the same room or halfway across the world.Â
A distributed computing system has numerous advantages over one that is centralized. Quick scalability is one such benefit, as more components can be added to the network whenever there is a need for increased bandwidth. Redundancy is another advantage offered by a distributed system. Another set on the same network can replace the processing power of any particular set of components facing downtime.
Yet another value proposition offered by distributed computing solutions is that they can be vendor agnostic. Such a system can operate on hardware sourced from numerous vendors. Moreover, software components based on differing standards can be compatible with the same distributed system. The system works independently from underlying software such as operating systems and communication protocols.
Enterprises use distributed computing systems to enhance the efficiency of their computer networks. For instance, a typical 3-tier distribution model sees the user interface being processed on employee endpoints, business processes being executed on a remote computer, and database processes being conducted in a centralized computing system that is accessed by numerous remote computers.
The client-server communications model is typically seen in such distributed computing environments. Many organizations rely on the popular distributed computing environment (DCE) industry standard to operate their distributed computing environments. Several third-party vendors also offer solutions that are compatible with this computing model.
Grid computing leverages a network of computers to perform tasks that would be difficult, if not impossible, for an individual machine. Grid computing systems can be homogeneous or heterogeneous and operate collectively as virtual supercomputers.
Topology in Grid Computing
When grid computing was first introduced, it was powered by the idea that computing power, like electricity, can be consumed from a grid. Today, this idea is reflected in the concept of cloud computing, while grid computing is better defined as a distributed network of collaborative computer systems.
The applications of grid computing include big data analysis and resource-intensive simulation. Grid systems are highly efficient at tasks that require high computing power, as all the connected computers pool their processing power, storage, and other capabilities together over the network. Some experts classify grid computing as a subcategory of distributed computing.
A grid computer operates as a virtual supercomputer made up of machines connected to a network, such as an Ethernet or the internet. Grid computing can also be classified as parallel computing, where multiple CPU cores are spread across numerous locations instead of operating on a single machine.
While grid computing, as a concept, is over two decades old, various organizations and industries follow different protocols â€” no standard rules for grid computing have been established or accepted globally. A grid computing system can consist of numerous computers with the same configuration (homogeneous network) or several different types of computers with differing configurations (heterogeneous network).
How Grid Computing Works
The architecture of a grid computing network consists of three tiers: the controller, the provider, and the user. The control node is usually a server, cluster of servers, or another powerful computer that administers the entire network and manages resource usage. The provider is a computer or group of computers that contribute processing power to the network. Finally, the user is the computer or set of computers that use network resources for executing operations.
The control node fulfills valid requests for available network resources from user computers and redirects processing power from provider computers as required. When a computer is not using resources, it is expected to contribute its processing power to the network resource pool. Therefore, computers on the network can switch between being providers and users based on the needs of the collective system.
Another essential component of a grid computing network is middleware, which allows for network-wide communication and enables different computers to execute an application or a process throughout the system. Middleware also authorizes functions before they are performed over the network, thereby offering protection from threats presented by unauthorized and potentially malicious processes.
Distributed computing and grid computing are closely related concepts that have some similarities along with differences. Below are ten key comparisons between the two computing systems.
Similarities between distributed and grid computing
Distributed computing and grid computing share several similarities. The top five similarities between these two types of computing have been listed below:
Distributed and Grid Computing Similarities
1. Resource sharing
Whether you use distributed computing or grid computing, you can access existing resources across every authorized computer in the network. Distributed and grid systems share hardware as well as software resources. Depending on the configuration, databases can also be accessed from any endpoint connected to the network.
Resource sharing means a reduction in cost and increased convenience for users. Also, data is easily synchronized, and all users have access to the latest version of information at all times.
Resource sharing is normally managed using a software solution, such as a resource manager. This allows users to set individual management methods and policies for every resource.
2. Heterogeneity & concurrency
A distributed or grid computing system supports heterogeneous data, software, sensors, devices, and networks. Different components such as operating systems, hardware, and programming languages can coexist on the same computing network. Enterprises can establish their own administrative and security policies that describe how such heterogeneous resources are used.
In addition to the heterogeneous nature of distributed and grid computing, these systems are concurrent. This means multiple processes can be executed simultaneously when using these computing systems. Different components execute various processes concurrently across multiple machines and communicate the results by interacting over the network. Concurrency minimizes latency and enhances the throughput of the computing system.
3. Openness & transparency
Distributed computing and grid computing systems are open and mappable. This means the specifications of the hardware and software deployed can be quantified and categorized, allowing users to predict system capabilities. As required, high openness allows easy improvement and extension of the computing system. Openness can be achieved by publishing a detailed interface of components that is standardized. This also makes it easier to find compatible components for scalability and redundancy purposes.
Closely related to openness is transparency â€” a property that allows both distributed and grid computing systems to be viewed as a whole unit instead of a collection of components operating simultaneously. High transparency enables programmers and users to access, operate, modify, and manage these systems without hassle.
The types of transparency are as follows:
- Location transparency: Location transparency allows stakeholders to trace or hide the physical location of an object within the system. Following a standardized and global naming convention makes it easy to achieve location transparency. One such naming convention could be assigning â€˜logical namesâ€™ to objects. This enables technical professionals to understand the location and role of the object easily, regardless of their tenure within the organization. Location transparency also enables end-users to access any copy of a resource without worrying about its storage location.
- Migration transparency: Distributed and grid systems leverage migration transparency to ensure high mobility of resources and processes. The software components can be migrated within the system without affecting existing operations and communications.
- Replication transparency: Replication is an essential feature of distributed and grid systems. Resources are replicated, and a copy is placed closer to the location from where it is accessed regularly to enhance availability and boost performance. Another advantage of replication is that it enables computing systems to deploy a copy of the resource to take over if another fails. Replication transparency allows users to trace the presence of copies of a resource and mask their existence from outside users. One method to achieve this is giving all copies the same name. Combining replication transparency with location transparency allows the system to reference copies stored at different locations even though they have the same name.
- Concurrency transparency: Sharing resources across the network is a crucial goal of distributed and grid computers. While sharing is cooperative in most cases, such as for communication processes, it can also be competitive, such as in cases where two users are accessing the same file in the same database. In the case of competitive sharing, concurrent transparency helps mask the fact that other users are accessing the same resource simultaneously.
- Failure transparency: Failure transparency in distributed and grid systems conceals from applications or users that an object within the system has failed to execute correctly. In the meantime, the error is automatically corrected.
Systems fail, and the more distributed a system, the higher the chance that something will go wrong. That is why both distributed and grid systems feature robust redundancy measures and are built to tolerate faults.
These computing environments have strong continuity measures at every tier, ensuring that no hardware or software failure can bring the entire system down. Applications leverage the connected nature of distributed and grid computing environments to fetch other copies of resources whenever the most convenient copy is unavailable. This enables the on-demand availability of critical resources even in the case of partial system failure.
Distributed and grid computing systems make it easy to scale up horizontally (by adding more components) and vertically (by upgrading existing components). As the number of users accessing the system increases, these computing environments can pull more processing power from the network to accommodate the increasing demand for bandwidth.
Key differences: Distributed vs. grid computing
Distributed computing and grid computing are used by organizations to create complex networks that can easily perform resource-intensive tasks. However, they have certain fundamental differences, as outlined below:
|Distributed Computing||Grid Computing|
|While there is a great deal of overlap in both systems, distributed computing has a broader definition.
The concept can be defined as any distributed computing system used to fulfill computational requirements. Such a computing environment can break down computing problems into several tasks. Each task is then solved by one or more machines connected to the distributed network. A distributed computing system is essentially any collection of independent machines that are interconnected over a network and capable of concurrently working on different tasks.
|On the other hand, grid computing has a slightly narrower definition.
The concept can be defined as the use of several computers, often geographically remote and connected using a network, to collaborate for accomplishing a predetermined task.Compared to distributed computing, which can work on numerous tasks simultaneously, some definitions of grid computing restrict it to a system that runs a computing grid or a collection of computers that communicate with each other directly to complete a single, highly resource-intensive taskâ€™.
Therefore, such a definition describes grid computing as a machine, such as a supercomputer, rather than a concept like distributed computing.
|2. Scope of application|
|Distributed Computing||Grid Computing|
|A distributed computing system is any collection of computers that work together. This setup may appear as a singular technological entity to the end-user.
The scope of application for distributed computing is broader than that of grid computing. Examples of distributed computing include global positioning systems (GPS), the internet, and global air traffic control (ATC) systems.
|As mentioned above, some experts classify grid computing as a subset of distributed computing.
In this case, grid computing would be a system that leverages computer resources from numerous administrative domains for completing a common goal. It has a narrower scope of application than distributed computing.
Such a system can be considered as a distributed computing system that features non-interactive workloads and large databases. This system works to execute specific computing tasks across a range of computers instead of a singular, centralized resource. Examples of grid computing include executing database queries and Perl scripts, big data analysis, and artificial intelligence (AI) modeling.
|3. Model and architecture|
|Distributed Computing||Grid Computing|
|Classifying distributed computing as a technological model means that it can have varying architectures, including grid computing, cloud computing, and cluster computing.
Cloud computing architecture consists of two main components: the frontend (which includes the client-facing aspect of the system and client infrastructure) and the backend (which includes the application, service, runtime, storage, infrastructure, management, and cybersecurity measures).
Cluster computing architecture consists of several individual computers that are interconnected and operating concurrently as a single computing system. Nodes connected over a network provide input, output, memory functions, and an operating system.
|Conversely, grid computing is a specific computing model with well-defined architectural components.
The fabric layer is at the base of the grid computing architecture and includes the basic resources, such as operating system, storage, processor time, internet bandwidth, and research tools like magnifiers and detectors.
The connectivity layer ensures swift and reliable access to the validation and interaction protocols that are necessary for transactions. This layer is the intermediary between the fabric and resource layers.
The resource layer enables functions such as execution, monitoring, and secure collaboration among assets.
The collective layer consists of commonly used services and collaborations. This layer manages resource exchange such as pre-allocation, file services, negotiations, planning, tracking, testing, and data duplication.
Finally, the application layer is at the highest level of the architecture and supplies users and administrators with an interface for communicating with the grid.
|Distributed Computing||Grid Computing|
|A distributed computing system operates using a group of computing elements that can either behave homogeneously or heterogeneously. These computing elements are also known as nodes and can be in the form of software or hardware.
Distributed systems consist of numerous nodesâ€”from powerful computers to tiny sensors. A core principle of distributed computing is that every node can operate independently. However, they are meant to exhibit synergy and help each other enhance the net throughput of the entire system, barring which, connecting them to the same network is pointless.
Nodes typically work together to achieve the goals of the collective system by communicating with each other.
|While grid computing can be either heterogeneous or homogeneous, supercomputers working on fixed computing tasks are more likely to be homogeneous in nature. This means that all the nodes throughout the grid are largely of the same configuration in terms of hardware, network, operating systems, administration, and security.
Recently, however, some grid computers are adopting a hybrid architecture that includes nodes explicitly chosen for certain tasks. The types of nodes in a grid computing system are normally chosen based on the overall goal of the grid.
|Distributed Computing||Grid Computing|
|Finally, the pricing structure of a distributed computing system can be either structured or unstructured.
For instance, cloud computing users pay a fixed, recurring fee for what they use. Fluctuations in this fee are predictable and depend on the metered billing measures of the vendor, which are tied to the resources consumed by the client.
|Since most grid computing setups are created by organizations themselves, the pricing structure of these systems is mostly unstructured. Generally, grid computing does not have a prescribed business model. Pricing often depends on the components deployed, which, in turn, depends on the goal of the grid computing system.|
Distributed and grid computer systems leverage the entire networkâ€™s collective memory, processing power, and storage to carry out computing tasks swiftly and efficiently. These systems enhance application performance, enable hassle-free data collection from various locations, and boost the availability and performance of IT resources. Regardless of their differences, distributed computing and grid computing have several business applications, especially in the heavily digitalized and vastly dispersed corporate structures of 2022.
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