Aaron Binders Are you trying to process massive datasets, run complex simulations, train machine learning models, or analyze large datasets efficiently? Good luck tackling those herculean tasks with a single server.
Cluster computing addresses these challenges by combining the processing power, memory, and storage resources of multiple computers. By distributing workloads across the cluster, performance and throughput are significantly increased.
This article will discuss everything about cluster computing, including how it works, its benefits, different types of clusters, server cluster hosting, and real-world examples of cluster computing implementations.
Table of contents
- What is cluster computing?
- How does a computer cluster work?
- The role of scheduling tools in cluster computing
- What is the difference between a computer cluster and grid computers?
- What are the benefits of a computer cluster?
- What are the types of cluster computing?
- Five examples of computer clusters
- Potential downtime
- Liquid Web: Cluster computing for data-intensive workloads
What is cluster computing?
A computer cluster is a set of connected computers that perform as a single system. These computers are basic units of a much larger system called a node.
A computing cluster can range from a simple two-node setup with personal computers to complex supercomputers with advanced architectures.
All units in the system are the same kind of machines. These computers are interconnected through fast and efficient local area networks (LANs) and generally use the same hardware. Their connection can be tight or loose, but they share one home directory.
How does a computer cluster work?
Clusters vary in size but share a common framework. A cluster typically has one or two head nodes and more computing nodes. The head node is where you log in, compile code, assign tasks, coordinate jobs, and monitor traffic across all nodes.
The computing nodes handle performance computing. They execute tasks, follow instructions, and function collectively as a powerful single system.
Tasks automatically move from the head system to the computing nodes, and excellent tools can help with workload scheduling. One of them is SLURM (Simple Linux Utility for Resource Management).
SLURM is a job scheduling program designed to meet the demanding needs of the computing process. It’s a workload manager that performs three responsibilities:
- Define the resource requirements for the given tasks.
- Set the proper environment for work.
- Specify those tasks and carry them out as shell commands.
To use tools like SLURM effectively, you need to know:
- How long are you planning to use the system?
- How much of your cluster do you really need?
The answer to the second question is the number of nodes and the number of given threads.
Each cluster node has one or more CPUs, typically with multiple cores.
“For instance, if you have one node with two processors containing 16 cores, the total core number is 32. This means that one of your computing nodes can perform 32 tasks simultaneously, even though a single core can do the job itself. The ability to perform more tasks using a cluster is the power of computer clusters.”
Fast, reliable, and low-latency networks are crucial for supporting parallel operations in a cluster.
The role of scheduling tools in cluster computing
Enduro/X is an app and middleware server in charge of distributed transaction processing. It’s an open-source platform, meaning the source code is freely available under a license. Then, the copyright holder gives users the right to study, change, use, or distribute the software to others.
Ganglia is a scalable monitoring tool for clusters, high-performance computing systems, or networks. It tracks valuable metrics such as network utilization and CPU load averages.
OpenHPC is a set of community-based tools that resolve regular tasks in HPC environments, provide documentation, and build blocks that can later be combined with HPC sites.
Apache Mesos is another open-source platform created to operate computer clusters.
A computer cluster is a set of two or more computers connected and able to perform as a single, united system. A node is the larger system, with connected computers acting as smaller units operating through local area networks.
What is the difference between a computer cluster and grid computers?
A grid operating system, such as SETI@home, is a network of independent computers distributed across various locations, working together to achieve a common goal. In a computer cluster, each node performs the same job. In a grid operating system, each node handles a different task.
Grid computing, valued at $3.6 billion in 2022, usually has numerous parallel computations that happen independently, so processors don’t need to communicate.
These projects are often distributed across many countries and connected through relatively fast LANs.
Grid computing is heterogeneous, with each node performing different tasks, while cluster computing is homogeneous, with nodes performing the same tasks.
What are the benefits of a computer cluster?
Businesses of all sizes use computer clusters. Why? Here are the top three benefits of cluster computing.
1. High-performance computing
Computer clusters offer higher performance and high-speed computing (HPC) at a lower cost than traditional hosting models. Tasks like engineering, scientific problems, and data-intensive processes require more performance than a single computer can provide, making HPC essential.
There are many cases when your app or site can experience latency, downtime, or other difficulties caused by high traffic and various elements. When you bundle more than one server with a load balancer, your traffic distributes across many machines, which helps your site or app perform well, even at its peak load.
This way, upcoming traffic hits a load balancer first, which intelligently distributes the traffic between machines. With a high-performance setup, you can add more servers and scale the system horizontally.
An HPC system typically consists of 16–64 computers, each with one–four processors. Each processor has two–four cores, resulting in a total of 64–256 cores. The system may be equipped with added hardware efficiency, like server GPUs. For HPC installations, both Windows and Linux are options, with Linux remaining the dominant operating system.
2. High availability
Another benefit of computer clusters is high availability (HA). High-availability hosting systems avoid service loss by minimizing planned downtime and managing failures.
High-availability servers also improve resource availability. If one computer fails, the others keep up with the process without interruptions. That helps prevent the potential loss of valuable information and time if a server fails.
3. Scalability and expandability
Clusters offer great scalability and expandability. As your user base grows, you can easily add resources to your cluster and distribute tasks across computers as needed.
Altogether, multiple computers always provide more significant processing power and increased system performance than a single computer.
What are the types of cluster computing?
Cluster computing comes in various forms, each optimized for different use cases.
Load-balancing clusters
In this computing cluster type, the system distributes workloads evenly across nodes to prevent any single node from becoming overwhelmed. They’re suitable for handling high-traffic web servers, applications with significant traffic demands, and scenarios requiring scalability.
Tiny clusters
These smaller clusters offer an affordable way for individuals to experiment with clustered environments or test proof-of-concept, although they have limited scalability compared to enterprise solutions.
High-performance computing (HPC) clusters
These clusters handle computationally intensive tasks such as scientific simulations, big data analytics, and AI model training. They typically employ specialized hardware with high-speed interconnects to minimize latency.
High-availability (HA) clusters
The primary goal of HA clusters is to ensure services and resources remain accessible without interruption. If one node fails, the cluster automatically transfers the workload to other nodes, preventing downtime for mission-critical applications, email servers, and databases.
Distributed clusters
This model involves clusters that span multiple geographical locations. Besides enhancing availability, it brings computing resources closer to end-users, reducing latency. With the appropriate networking infrastructure, even cloud or serverless environments can function as distributed clusters, offering scalability and flexibility.
5 examples of computer clusters
Computer clusters are used across various industries for heavy computational tasks. Here are five examples of computer cluster use cases.
1. Support with nuclear simulations
Handling the thermo-mechanical properties of nuclear and structural materials is important for safely operating nuclear facilities. Tools people use here need to deal with various length scales and time. High-speed memory systems, HPC, and hardware designed for parallel computation reduce computing times.
2. Weather forecasting system
Weather prediction systems use models that compute changes based on physics, chemistry, and fluid dynamics. The model’s fidelity, algorithms, and number of represented data points tell how precise and accurate a forecast will be.
3. Database servers
A single server may not handle all data or requests efficiently, making database clustering necessary. That’s why you may need database hosting. That’s the process of connecting a single database by combining two or more servers or instances.
4. Aerodynamics
Airplane takeoffs are noisy because aerodynamic drag and engine output approach the maximum when the plane picks up speed. HPC helps engineers to optimize the aerodynamics and simulate it in around two hours. That way, engineers manage to achieve the environmental and financial goals of their industry.
5. Data mining
In data clustering, similar data objects are grouped. After dividing data and classifying the groups, you label them according to their features. Clustering here helps simplify all further steps within the data mining process.
Potential downtime
As mentioned before, high-availability (HA) clusters are particularly important because they reduce the possibility of downtime.
Downtime is when your computer or entire system is unavailable, non-operational, or offline. There are plenty of things that can potentially cause it, such as:
- Scheduled downtime.
- Human errors.
- Hardware or software malfunctions.
- Environmental disasters (fires, floods, temperature changes, or power outages).
Downtime can result in data loss, reputational damage, security breaches, and financial loss, making high availability important for business continuity.
Liquid Web: Cluster computing for data-intensive workloads
Whether you’re up to building social media apps or simply want to scale your ecommerce business, Liquid Web has server cluster options that can help you get there. Our clusters are easily scalable and precisely created to fit your needs.
Contact us for managed services for your mission-critical applications, including high-performance hosting and high-availability hosting. Or check out our server cluster plans to get started today.
Note on the original publish date: This blog was originally published in April 2021. It has since been updated for accuracy and comprehensiveness.