Grid computing is a computing infrastructure wherein computers in different geographical locations are connected together to work on common tasks. These computers do not need to be in the same building or even the same country, so long as they are connected through a network (locally or over the cloud).
Within a grid computing network, one of the computers acts as the control that assigns tasks to the rest of the computers. In a grid computing setting, all computers can communicate with the control computer but not with other computers in the network.
3 Types of Grid Computing
- Computational Grid Network
- Scavenging Grid Network
- Data Storage Grid Network
One of the most common uses of grid computing is in weather forecasting. To make predictions about the weather, meteorologists need to collect massive amounts of data from various locations and then compute those data points together. Handling that much data can be too time consuming for just one computer.
That said, time efficiency is not the only advantage of using grid computers. They also allow users to access computational resources unavailable in the current physical location and store their files in the network for others to access.
Components of Grid Computers
Nodes are the computers or servers that are connected together to form a grid computing network. Each node has different (and common) resources, such as a GPU (graphical processing unit) and memory. Technically, there’s no limit to how many nodes are involved in a grid computing network.
There are three types of nodes: control, provider and user nodes.
Grid middleware is a specialized software that connects and controls computing resources in the grid network. The software handles the requests from the user nodes, manages resources and provides security to the system by preventing misuse of results and resources.
Grid Computing Architecture
Grid architecture represents the structure of each computer within the network. Each node has four layers:
- The top layer comprises high-level applications, such as software for specific tasks.
- The middleware layer manages and allocates resources.
- The third layer is the collection of available resources such as CPU and memory.
- The bottom layer is the connection between the nodes in the grid network.
How Does Grid Computing Work?
A grid computing network is a collection of computers working together; we call each computer a node. We categorize nodes based on the task they perform in the network and there are three types of nodes in grid computing.
A control node is the network administrator and the computer that manages the allocation of resources within the network. When the user node requests resources, the control node checks resource availability. Once the control node finds an available resource, it assigns the given task to the node handling that resource. The grid computing software (middleware) also runs on the control node.
A user node is a computer that requests resources from other nodes in the grid to perform the task assigned to it by the control node. Each request goes through the control node for approval. The results are then shared with other nodes or the control node based on the assigned task.
A provider node is a node that shares the resources with user nodes when requested. Provider nodes often perform smaller tasks for the user node and then send the results to the user and control nodes.
In the system, nodes aren’t fixed. Instead, they can be a user or provider node based on the task the system is trying to accomplish.
Types of Grid Computing
1. Computational Grid Network
A computational grid computing network consists of high-performance computers, such as supercomputers.
Computational grids are best for time-sensitive applications. For example, a computational grid can speed up a companies’ business report generation within seconds or modify stock market predictions in real-time using the nodes in the network with high computational power. This results in a huge performance increase over traditional systems.
2. Scavenging Grid Network
CPU scavenging grid networks have many regular computers as well as a few high-performance ones. Scavenging describes the process of searching for available resources in a network of regular computers. We also call this type of grid network CPU scavenging or cycle scavenging.
CPU scavenging networks allow users in different geographical locations to access documents simultaneously; for example, users can access and edit documents, designs and other files simultaneously.
While this may sound similar to how you collaborate with your co-workers through a program like Google Drive, there are key differences. Google is a service provider, so people pay to access the data and we can do it from any device through web browsers. However, grids are organization-based and individuals don’t often don’t pay for them (because they are within an organization). Also, grid devices are less accessible. You can only access them through middleware. Grid computers are also much less scalable than cloud infrastructure.
3. Data Storage Grid Network
A data storage grid is a network connecting computers in order to provide large data storage capacity that users can access easily from any node in the network. We might use a data storage grid when we need to coordinate sharing data access between all users in the grid network. We can use this type of grid network as storage for websites with a global user base such as universities and research institutes.
Advantages of Grid Computing
Time and Resource Efficiency
Using grid computing allows for breaking down an enormous, complex task into smaller, easy-to-handle tasks performed on different computers simultaneously. In doing so, the computations’ results are reached within shorter time frames and with an efficient use of available resources.
Grid computing allows the reuse of existing hardware resources to handle different tasks. Doing so eliminates the need to purchase extra resources since grid computing allows using resources on the cloud if the necessary hardware is in other physical locations.
Since grid computing networks are structured across different physical locations, this practice provides flexibility to users and allows them to collaborate and use hardware resources located in different geographical locations.
Limitations of Grid Computing
A grid network’s middleware requires constant optimization. Because the middleware is just like any other software, there are always the risk of bugs or something not working the way it is intended to work. Moreover, since the type of middleware used depends on the capabilities of the control node, if we update the hardware, the middleware needs to change accordingly.
In order to make grid computers more efficient, systems require a super-fast interconnect between different computer resources in order to harness the full potential of the grid. Speed is one of the main reasons we use grid computing so having a slow connection between nodes defeats the purpose of using grid computers.
Control Node Sensitivity
Trouble in the control node can cause the entire network to halt. If the middleware on the control node crashes, or the control node loses power, the network won’t be accessible until the control node is fully functioning again.
Applications of Grid Computing
Financial services use grid computing to calculate and handle risk management. Using grid networks allows financial offices from different locations to send data to the control node, which then assigns it to the hardware with the highest computational power to calculate risk in a timely manner.
The gaming industry uses grid computing to provide additional computational resources for game developers. Game development requires heavy image and video processing in high-quality graphics, which requires a lot of computational power that’s often unavailable on a developer’s personal computer.
Film and Television
Film production companies sometimes produce movies with complex special effects. Creating these effects can require a powerful computer; grid computers help make that process more efficient. A production company may have a limited number of high-performance computers that everyone can access through the grid network to process their work.
Different types of engineers (electrical, industrial, chemical, software) use grid computing to create models, analyze designs and run complex simulations of various applications. For example, electrical engineers can use grid computing to run simulations of large circuits before building them. Similarly, a chemical engineer can use a grid network to access high-performance computing in order to check different chemical interactions between elements and develop a particular treatment (such as when scientists were developing the Covid-19 vaccine).
Scientific researchers use grid computers to access high-performing computers on the cloud in order to perform complex tasks they can’t handle locally. In university research institutes, there are often one or a few supercomputers that researchers can access through the organization’s internal network. This is useful because scientific research often requires handling vast amounts of data that personal computers can’t store or handle.