Catalog
concept#Architecture#Software Engineering#Observability

Resource Optimization

Strategy for efficient use and allocation of technical resources, focusing on performance, cost and reliability.

Resource Optimization denotes strategies for efficient use of constrained IT resources (CPU, memory, network, storage) via analysis, prioritization and adjustment of allocations.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Monitoring and observability tools (e.g. Prometheus, Grafana)Orchestration platforms (e.g. Kubernetes)Cost management services (e.g. cloud billing APIs)

Principles & goals

Make metric-driven decisionsIterative, controlled rollout of changesSeparate capacity planning from configuration
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Excessive downsizing can harm availability
  • Misinterpreting transient spikes leads to wrong decisions
  • More complex operations due to added rule sets
  • Conservative adjustments with monitoring safeguards
  • Scenario and stress testing before production rollout
  • Regularly validate recommendations against actual costs

I/O & resources

  • Observability data (metrics, traces, logs)
  • Cost and billing data
  • Service-level requirements and priorities
  • Recommended resource configurations
  • Automated scaling rules
  • Reports on cost and performance

Description

Resource Optimization denotes strategies for efficient use of constrained IT resources (CPU, memory, network, storage) via analysis, prioritization and adjustment of allocations. It combines architectural principles, monitoring data and automated actions to improve cost, performance and reliability in operation. Scope spans from application level to cloud infrastructure.

✔

  • Lower operating costs through more efficient resource use
  • Better performance and more stable SLAs
  • Early detection and elimination of hotspots

✖

  • Requires stable observability data
  • Initial analysis effort and tooling costs
  • Not all workloads can be scaled automatically

  • Utilization (CPU/Memory)

    Average and peak utilization to evaluate over-/underprovisioning.

  • Cost per workload

    Direct mapping of infrastructure cost to applications or services.

  • SLA attainment and error rates

    Measure adherence to performance and availability targets.

Right-sizing a microservice environment

Case: Reduced cost by adjusting CPU and memory limits while maintaining performance.

Autoscaling for spiky workloads

Implementing combined horizontal and vertical scaling for volatile load.

Rescheduling a batch pipeline

Optimized execution windows and resource orchestration to avoid collisions and bottlenecks.

1

Define goals and KPIs for resource usage.

2

Collect and normalize relevant metrics.

3

Perform analyses and derive optimization recommendations.

4

Implement automated rules and introduce them gradually.

⚠️ Technical debt & bottlenecks

  • Missing resource tagging complicates attribution
  • Outdated monitoring with insufficient resolution
  • Team silos prevent consistent policies
CPU bottlenecksMemory fragmentationI/O and network latencies
  • Automatically removing reservations during critical business hours
  • Reducing resources based on insufficient or misleading metrics
  • Overgeneralized rules treating diverse workloads the same
  • Over-focus on cost without checking SLAs
  • Missing seasonality analysis leads to wrong adjustments
  • Ignoring interference between services on shared resources
Knowledge in observability and metric interpretationExperience with cloud and container orchestrationBasics in performance and capacity planning
Cost optimizationPerformance requirementsOperational reliability
  • • Limited visibility without adequate observability setup
  • • Regulatory or compliance requirements in multi-tenant environments
  • • Legacy systems with rigid resource requirements