Docker

Docker is a container platform that lets applications, including their dependencies, run isolated within lightweight software containers. Within modern OT and Industrial Automation environments, Docker is increasingly used for Edge Computing, industrial data integration, protocol gateways, MQTT brokers, OPC UA services, analytics and Soft PLC platforms.

Docker plays an important role in the shift from traditional hardware-based OT infrastructures to software-defined and cloud-native automation architectures. This brings IT platforms and industrial control environments closer together within IT OT Convergence.

Unlike classic Virtualisation, Docker does not virtualise full operating systems but rather applications and processes. This makes containers significantly lighter, faster and more scalable than traditional virtual machines.

⚙️ Basic principles of Docker

Docker uses containerisation to run applications isolated within shared kernel resources of the host operating system.

A Docker container contains:

• application code
• libraries
• runtime dependencies
• configuration
• network definitions
• file systems

Architecture:

Application
     │
Docker Container
     │
Docker Engine
     │
Host Operating System
     │
  Hardware

Unlike a Hypervisor, Docker shares the kernel of the host OS.

Benefits:

• low overhead
• fast startup
• efficient resource use
• high scalability

🏗️ Important Docker components

📦 Docker Images

A Docker image contains all components needed to run an application.

Images consist of layers:

Base OS Layer
      │
Runtime Layer
      │
Application Layer
      │
Configuration Layer

Benefits of layered images:

• efficient storage
• fast distribution
• caching
• reusability

Within OT, images are often used for:

• MQTT brokers
• OPC UA servers
• historians
• protocol converters
• edge analytics
• dashboards
• AI workloads

🧠 Containers versus virtual machines

Within OT, containers are often combined with Virtualisation.

⚡ Docker within OT

Docker is increasingly used in Industrial Automation.

Typical OT workloads

📡 Docker and Edge Computing

Within Edge Computing, Docker is virtually dominant.

Edge devices often run multiple containers:

Edge Device
 ├── MQTT Broker
 ├── OPC UA Gateway
 ├── Historian
 ├── AI Analytics
 └── Dashboard

Benefits:

• local processing
• protocol conversion
• offline operation
• easy updates
• scalability

Containers make edge platforms very flexible.

🔌 Industrial communication

Docker containers commonly communicate via:

• MQTT
• OPC UA
• HTTP
• HTTPS
• Modbus TCP
• REST APIs

Common OT container services:

☁️ Docker and cloud-native OT

Docker forms the foundation for cloud-native OT architectures.

Important concepts:

• microservices
• immutable infrastructure
• Infrastructure as Code
• CI/CD
• orchestration
• edge-native OT

Containers CAN run:

• on-premises
• on edge gateways
• in private cloud
• in public cloud
• in hybrid OT environments

🔄 Docker Compose and orchestration

For multi-container applications, Docker Compose is often used.

Example:

SCADA Stack
 ├── MQTT Broker
 ├── Historian
 ├── Grafana
 └── OPC UA Gateway

Larger environments use orchestration platforms:

• Kubernetes
• K3s
• OpenShift
• Docker Swarm

Within OT, lightweight orchestration at the edge is growing especially fast.

🧪 Test and OTAP environments

Docker is ideal for:

• OTAP
• development environments
• simulation
• test labs
• protocol testing
• digital twins

Benefits:

• reproducible environments
• fast deployment
• rollback options
• easy snapshots

This allows engineers to simulate complex OT stack environments locally.

⚡ Performance considerations

Containers usually deliver better performance than traditional Virtualisation.

Benefits

Possible bottlenecks

• storage IO
• network overlay Latency
• container density
• CPU contention
• orchestration overhead

Real-time OT workloads require careful tuning.

🧠 Docker and real-time OT

Containers are not real-time by default.

Challenges:

• scheduler latency
• kernel contention
• interrupt handling
• timing variation

For real-time applications, the following are used:

• real-time Linux
• CPU pinning
• isolated cores
• real-time scheduling
• TSN networks

For high-speed Motion Control, dedicated real-time platforms often remain necessary.

🔒 Cybersecurity risks

Docker introduces new OT Security challenges.

Important risks

Containers significantly enlarge the attack surface of OT environments.

🛡️ Hardening of Docker in OT

Important security measures:

• minimal base images
• image signing
• immutable containers
• non-root containers
• RBAC
• MFA
• network isolation
• secrets management
• image scanning
• runtime monitoring

Additional OT measures:

• Industrial Firewall
• Network Segmentation
• Logging
• Security Monitoring
• Application Whitelisting

📦 Docker registries

Images are stored in registries.

Public registries

• Docker Hub
• GitHub Container Registry

Private registries

Within OT often required because of security requirements.

Benefits:

• controlled images
• supply-chain control
• internal validation
• Compliance

🧩 Docker within Unified Namespace architectures

Docker is widely used within Unified Namespace environments.

Typical stack:

MQTT Broker
      │
Sparkplug Gateway
      │
   Historian
      │
   Analytics
      │
   Dashboard

Each component runs as a separate container.

Benefits:

• scalability
• independent management
• easy updates
• fault isolation

🏭 Practical applications

Manufacturing

Docker for:

• OEE dashboards
• protocol gateways
• AI Vision processing
• MQTT infrastructure

Energy supply

Applications:

• energy analytics
• edge Telemetry
• Predictive Maintenance

Building Automation

Containers for:

• HVAC analytics
• energy monitoring
• smart building integration

Water sector

Use for:

• remote telemetry
• edge buffering
• data forwarding

⚠️ Operational considerations

Persistent storage

Containers are stateless by default.

OT systems, however, require persistent data for:

• historians
• alarms
• Audit logs
• trends

Volumes are therefore used.

Lifecycle Management

Important considerations:

• image versioning
• dependency management
• patching
• rollback procedures
• Vulnerability Scanning

Integration with:

• Configuration Management
• Patch Management
• Version Control

🛡️ Relevant standards and frameworks

Container security is becoming more important within industrial compliance requirements.

📈 Trends and developments

Important trends:

• cloud-native OT
• containerised SCADA
• edge orchestration
• AI at the edge
• microservices in OT
• Kubernetes for industry
• software-defined automation
• event-driven architectures

Docker is a core component of modern industrial software architectures.

🎯 Conclusion

Docker has made containerisation accessible for industrial automation and plays a growing role within modern OT environments. Containers enable flexible, scalable and reproducible OT platforms for edge computing, industrial data integration and cloud-native automation.

Within IT OT Convergence, Docker is an important building block for software-defined industrial infrastructures, but successful implementation requires attention to real-time behaviour, persistent storage, cybersecurity and lifecycle management.

For modern edge and IIoT architectures, Docker provides a powerful foundation for flexible and scalable OT applications.