Linux cluster systems are a critical part of the infrastructure of many businesses and research institutions. Depending on the purpose and usage, they can be broadly classified into 4 types. Let’s have a look at the characteristics and real-world use cases for each of these types.

What is a Cluster System?

A cluster system is a group of computers connected through a network that act as a single system. It can be set up for various purposes such as high performance, high availability, and load balancing.

1. High Availability (HA) Cluster

These are used in mission-critical systems in businesses that need to provide services 24/7 without any downtime.

Key Characteristics

  • Automatically switches over to another system in case of a failure
  • Can be configured as Active-Active or Active-Standby
  • Monitors the health of the system with real-time monitoring

Use Cases

  • Core banking systems in the financial industry
  • E-commerce platforms
  • Enterprise mail servers

2. Beowulf Cluster

These are low-cost, high-performance clusters that are built by connecting commodity PCs to achieve supercomputing-like performance.

Key Characteristics

  • Uses commodity hardware to reduce costs
  • Based on Linux, eliminating the need for licensing costs
  • Simple network configuration using Ethernet

Use Cases

  • Simulation environments in university labs
  • R&D centers in small and medium businesses
  • Educational cluster systems

3. HPC (High-Performance Computing) Cluster

These are supercomputing-class clusters that provide the highest levels of computing power.

Key Characteristics

  • Uses high-performance processors and ultra-fast networks
  • Capable of massively parallel processing
  • Provides high-performance distributed storage

Use Cases

  • Weather forecasting systems in meteorological departments
  • New drug development environments in pharmaceutical companies
  • Crash simulations in automobile companies

4. HTC (High-Throughput Computing) Cluster

These are clusters optimized for efficiently handling large numbers of jobs.

Key Characteristics

  • Processes multiple independent jobs simultaneously
  • Provides efficient job scheduling
  • Easy to scale

Use Cases

  • Big data analytics platforms
  • Render farms
  • Large-scale log analysis environments