Catalog
concept#Platform#DevOps#Observability

Container Orchestration

Architectural concept for automating deployment, scaling and management of containerized applications across clusters.

Container orchestration coordinates the lifecycle of containerized applications across multiple hosts, including scheduling, scaling, service discovery and fault handling.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Container registries (e.g. Docker Hub, GCR)CI/CD systems (e.g. Jenkins, GitLab CI)Cloud providers and on-premises infrastructures

Principles & goals

Declarative desired-state configuration instead of imperative commandsFavor container ephemerality and immutable imagesSeparate control plane and data plane
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Misconfigurations can cause large outages
  • Security vulnerabilities in platform components
  • Resource contention and limited isolation
  • Use declarative manifests and GitOps workflows
  • Automate health checks and liveness probes
  • Implement resource requests and limits

I/O & resources

  • Container images in registry
  • Deployment definitions (manifests)
  • Cluster infrastructure (nodes, network, storage)
  • Deployed, scaled services
  • Status and health metrics
  • Service endpoints and routes

Description

Container orchestration coordinates the lifecycle of containerized applications across multiple hosts, including scheduling, scaling, service discovery and fault handling. It abstracts infrastructure details, enables declarative operation and simplifies DevOps workflows. Decisions involve trade-offs between performance, reliability, operational complexity and cost.

✔

  • Automatic scaling and self-healing of applications
  • Portability across infrastructures
  • Simplified operations through declarative operating models

✖

  • Complexity and high operational overhead
  • Challenges with stateful workloads
  • Dependence on ecosystem and platform implementation

  • Mean Time To Recovery (MTTR)

    Average time to recover after a service failure.

  • Pod start time

    Time from scheduling to a running container.

  • Cluster resource utilization

    Utilization levels of CPU, memory and storage in the cluster.

Kubernetes for microservices

Use of a Kubernetes cluster architecture to orchestrate numerous microservice components with automatic scaling and service discovery.

StatefulSets for stateful services

Use of StatefulSets and persistent volumes to manage stateful workloads such as databases within the orchestrator.

Edge clusters for distributed workloads

Combination of central and edge clusters to run latency-sensitive services close to users with central control.

1

Assess needs and choose an orchestrator platform

2

Provision cluster and apply base configuration

3

Set up deployment pipelines and observability

4

Define roles, policies and resource quotas

5

Train operations and development teams

⚠️ Technical debt & bottlenecks

  • Manual scripts instead of declarative configurations
  • Non-standardized deployment templates
  • Outdated orchestrator versions without an upgrade plan
Network latencyPersistent storageObservability and monitoring gaps
  • Using it merely as virtualization without automation
  • Scaling critical stateful services without a persistence strategy
  • Exposed admin APIs without role and network policies
  • Underestimating observability requirements
  • Missing backup strategies for persistent data
  • Complex network topologies without clear documentation
Container and image conceptsKubernetes or orchestrator operationsNetworking and storage fundamentals
Scalability and elastic resourcesAvailability and self-healingPortability across environments
  • • Infrastructure resource limits
  • • Legacy applications that are not containerized
  • • Regulatory requirements for data residency