Catalog
concept#Architecture#Software Engineering#Observability#Reliability

Adaptation

Adaptation is the architectural principle of adjusting systems at runtime to changed conditions to maintain availability and performance.

Adaptation describes the capability of systems and architectures to dynamically adjust behavior, configuration, or topology in response to internal state changes or external environmental conditions.
Emerging
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Metrics and logging systems (Prometheus, ELK)Orchestration platforms (Kubernetes)Feature flag and configuration systems

Principles & goals

Define explicit metrics that drive adaptation decisions.Implement feedback loops with clear timings and stabilization.Separate monitoring, analysis, planning and execution (MAPE loop).
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Oscillation due to insufficient damping or too aggressive rules.
  • Hidden side effects when changing topology or configuration.
  • Lack of transparency hampers root-cause analysis.
  • Introduce gradually with canary phases and rollback mechanisms.
  • Careful tuning of hysteresis and damping to avoid oscillations.
  • Comprehensive logging and auditability of automated actions.

I/O & resources

  • Runtime metrics (CPU, latency, errors)
  • SLO/SLA targets
  • Configuration and policy definitions
  • Configuration changes or scaling actions
  • Alerts and audit logs
  • Metric changes to evaluate effect

Description

Adaptation describes the capability of systems and architectures to dynamically adjust behavior, configuration, or topology in response to internal state changes or external environmental conditions. The goal is to preserve robustness, availability and performance. It includes design principles, runtime control, observability and feedback loops for decision making.

✔

  • Increased availability through automatic reaction to failures.
  • Better resource utilization through dynamic adjustment.
  • Faster response to changing load and environmental conditions.

✖

  • Increased implementation and operational effort.
  • Potential instability with poorly calibrated rules.
  • Not all problems are suitable for automatic runtime adaptation.

  • Mean Time To Recover (MTTR)

    Time to recover a failing service after an automated adaptation.

  • Adaptation frequency

    Number of adaptations per time unit as an indicator of responsiveness.

  • Stability rate

    Share of adaptations that cause no negative effects within a defined stabilization period.

Autonomic Computing concepts at IBM

IBM developed autonomic computing as a framework for self-managing systems with monitoring and reaction mechanisms.

Autoscaling in Kubernetes

Kubernetes Horizontal Pod Autoscaler adjusts replicas at runtime based on defined metrics.

Feature-flag based rollbacks

Feature flags enable fast, controlled behavior adjustment without deployments.

1

Identify metrics and set up observability pipeline.

2

Define adaptation goals and thresholds, including stabilization times.

3

Implement, test and roll out automation rules incrementally.

⚠️ Technical debt & bottlenecks

  • Ad-hoc rules without centralization hinder maintenance.
  • Insufficient documentation of adaptation logic.
  • Lack of automated tests for adaptive scenarios.
Telemetry pipeline bottlenecksControl path latencyInsufficient metric sampling rate
  • Scaling solely due to a short-lived metric spike without smoothing.
  • Automatically disabling critical functions without fallback.
  • Using adaptive rules to cut costs at the expense of availability.
  • Underestimating observability costs for fine-grained rules.
  • Missing test coverage for adaptive paths.
  • Blurring responsibilities between automation and ops teams.
System and architecture understandingMonitoring and observability skillsExperience with rule sets and control algorithms
Latency and throughput requirementsAvailability targets and SLAsOperational observability and control
  • • Limited measurability of business metrics in real time.
  • • Regulatory requirements may forbid certain automatic adjustments.
  • • Incompatible configuration models between components.