Kubernetes

Kubernetes is an open-source container orchestration platform for managing, scaling, automating and orchestrating containerised applications. Within modern OT and Industrial Automation environments, Kubernetes is increasingly used for Edge Computing, industrial data integration, analytics, MQTT infrastructures, AI workloads and cloud-native automation platforms.

Kubernetes is the next step beyond Docker by not only running containers but also automating the management of complete container platforms. This creates a scalable and software-defined OT infrastructure that aligns with modern IT OT Convergence architectures.

Within industrial environments, Kubernetes is used for:

• edge orchestration
• IIoT platforms
• containerised SCADA components
• data pipelines
• AI inferencing
• protocol gateways
• MQTT infrastructures
• OT analytics

Kubernetes thus plays a central role within cloud-native OT.

⚙️ What is Kubernetes

Kubernetes — often abbreviated as K8s — automates:

• container deployment
• scaling
• networking
• failover
• Lifecycle Management
• load balancing
• service discovery

Where Docker manages individual containers, Kubernetes manages complete container clusters.

Architecture:

Applications
      │
  Containers
      │
  Kubernetes
      │
    Nodes
      │
Infrastructure

Kubernetes fully abstracts the underlying infrastructure.

🏗️ Kubernetes architecture

A Kubernetes cluster consists of multiple components.

Control Plane

The central management layer.

Worker Nodes

Run containers.

Important components:

📦 Important Kubernetes concepts

Pods

The smallest executable unit within Kubernetes.

A Pod contains:

• one or more containers
• network stack
• storage mounts

Deployments

Manage container rollout.

Functions:

• rolling updates
• automatic Recovery
• scalability
• versioning

Services

Network abstraction for applications.

Supports:

• load balancing
• service discovery
• internal routing

Namespaces

Logical separation within clusters.

Used for:

• OT segmentation
• multi-tenancy
• Security boundaries

☁️ Kubernetes within OT

Kubernetes is growing rapidly within industrial edge and data environments.

Typical OT workloads

📡 Kubernetes and Edge Computing

Within Edge Computing, lightweight Kubernetes variants are commonly used.

Examples:

• K3s
• MicroK8s
• KubeEdge

Typical edge architecture:

Sensors/PLC
     │
Edge Node
 ├── MQTT
 ├── OPC UA
 ├── AI
 └── Historian

Benefits:

• local processing
• offline functionality
• central orchestration
• remote updates
• scalability

Edge Kubernetes clusters CAN manage hundreds of OT sites.

🔌 Industrial communication

Kubernetes often runs OT protocols in containers.

Commonly used protocols:

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

Protocol gateways often function as microservices.

🧠 Kubernetes and microservices

Kubernetes encourages microservice architectures.

Traditional OT systems were often monolithic:

SCADA Application
 ├── Historian
 ├── Alarming
 ├── HMI
 └── Reporting

Cloud-native OT splits functions:

MQTT Service
Historian Service
Alarm Service
Analytics Service
Dashboard Service

Benefits:

• independent scaling
• fault isolation
• faster updates
• better flexibility

🔄 Kubernetes orchestration

Kubernetes automates operational tasks.

Important functions

This significantly reduces manual management.

⚡ High Availability

Kubernetes supports high availability.

Capabilities

• multi-node clusters
• automatic failover
• redundant services
• load balancing
• distributed storage

Within critical OT environments, HA architectures are essential.

🖥️ Kubernetes and SCADA

Full SCADA systems are still containerised only to a limited extent, but sub-components are.

Containerisable components:

Real-time process control often remains outside Kubernetes because of deterministic requirements.

⚠️ Real-time limitations

Kubernetes was originally designed for IT workloads, not for hard real-time OT.

Issues:

• scheduler Latency
• network overhead
• container Jitter
• orchestration delays
• resource contention

For Motion Control and Safety systems, dedicated real-time infrastructure often remains necessary.

🔒 Cybersecurity risks

Kubernetes introduces a large attack surface.

Risks

OT environments therefore require strict Hardening.

🛡️ Kubernetes hardening in OT

Important measures:

• minimal container privileges
• immutable containers
• signed images
• private registries
• network policies
• Microsegmentation
• secrets management
• Audit Logging

Additional OT security:

• Industrial Firewall
• Network Segmentation
• MFA
• RBAC
• Security Monitoring
• Logging

📦 Kubernetes networking

Kubernetes uses software-defined networking.

Important components:

Within OT, network Virtualisation can have impact on:

• Latency
• Jitter
• Deterministic Behaviour

Industrial networks therefore require careful tuning.

🧪 Kubernetes for OT test environments

Kubernetes is highly suitable for:

• OTAP
• test environments
• simulation
• Digital Twin
• cyber ranges
• AI experimentation

Benefits:

• reproducibility
• fast deployment
• rollback options
• automated provisioning

📡 Unified Namespace and Kubernetes

Within Unified Namespace architectures, Kubernetes often runs:

• MQTT brokers
• Sparkplug services
• historians
• dashboards
• analytics pipelines

Example:

Kubernetes Cluster
 ├── MQTT Broker
 ├── Sparkplug Gateway
 ├── Historian
 ├── AI Analytics
 └── Dashboarding

This creates a scalable OT data fabric.

☁️ Hybrid cloud and OT

Kubernetes supports hybrid architectures.

Workloads can run:

• on-premises
• at the edge
• in private cloud
• in public cloud

Benefits:

• workload portability
• central orchestration
• hybrid OT/IT integration

Important within multi-site industrial organisations.

⚡ Performance considerations

Benefits

Possible bottlenecks

• storage latency
• overlay networking
• container density
• orchestration overhead
• etcd performance

For OT systems, performance profiles must be tested carefully.

🛠️ Lifecycle management

Kubernetes supports modern software processes.

Important capabilities:

• CI/CD
• GitOps
• declarative configuration
• Infrastructure as Code
• rolling deployments
• automatic updates

Integration with:

• Version Control
• Configuration Management
• Patch Management

🏭 Practical applications

Manufacturing

Use for:

• edge analytics
• machine monitoring
• AI Vision systems
• OEE platforms

Energy supply

Applications:

• Predictive Maintenance
• load analytics
• Telemetry processing

Water sector

Use for:

• remote telemetry
• distributed analytics
• Historian aggregation

Building Automation

Container platforms for:

• HVAC analytics
• smart building services
• energy dashboards

🛡️ Relevant standards and frameworks

Container orchestration increasingly falls under OT Security Policy.

📈 Trends and developments

Important trends:

• cloud-native OT
• Kubernetes at the edge
• GitOps for OT
• AI orchestration
• software-defined automation
• industrial data fabrics
• containerised SCADA
• edge-native analytics

Kubernetes is growing into a core platform for modern industrial software architectures.

🎯 Conclusion

Kubernetes forms a fundamental building block for cloud-native and software-defined OT architectures. The platform enables scalable orchestration of containerised applications within edge computing, industrial analytics and modern data integration platforms.

Within IT OT Convergence, Kubernetes offers powerful capabilities for automation, scalability and lifecycle management, but successful implementation requires attention to real-time behaviour, cybersecurity, network architecture and OT reliability.

For modern edge and IIoT environments, Kubernetes is rapidly developing into the standard platform for container orchestration within Industrial Automation.