Description

This curriculum spans the technical, organizational, and governance dimensions of capacity management, reflecting the scope and complexity of multi-workshop programs used to establish enterprise-wide capacity governance and integrate performance modeling into service lifecycle decisions.

Module 1: Strategic Capacity Planning and Business Alignment

Define service capacity thresholds based on business growth projections and SLA commitments, requiring negotiation with finance and business unit leaders to validate assumptions.
Select between predictive (forecast-based) and reactive (on-demand) capacity models depending on application criticality and cost tolerance, balancing over-provisioning risks with performance guarantees.
Map transactional workloads from business services to underlying IT components using dependency modeling tools, ensuring accurate representation of capacity impact across tiers.
Establish capacity review cadence with business stakeholders to reassess demand drivers, such as new product launches or regulatory changes, that affect infrastructure requirements.
Integrate capacity planning into the service portfolio management process to align technology investment with service lifecycle phases and retirement schedules.
Decide on the inclusion of shadow IT systems in capacity models when they contribute to workload on shared infrastructure, despite lack of formal governance oversight.

Module 2: Workload Characterization and Performance Baselines

Instrument application and infrastructure layers to collect granular performance metrics (e.g., CPU per transaction, IOPS per user session) for baseline establishment.
Differentiate between peak, average, and sustained workloads for each service, using historical data to identify seasonal or cyclical patterns.
Classify workloads by type (batch, interactive, real-time) to apply appropriate measurement techniques and performance criteria.
Normalize performance data across heterogeneous environments (e.g., virtual vs. physical, cloud vs. on-prem) to enable consistent comparison and trend analysis.
Address data gaps caused by monitoring blind spots or uninstrumented legacy systems by deploying synthetic transactions or log-based extrapolation.
Validate baseline accuracy through correlation with incident records, particularly performance-related outages or slowdowns.

Module 3: Capacity Modeling and Simulation Techniques

Choose between queuing theory models and simulation tools based on system complexity and data availability, accepting trade-offs in precision versus implementation effort.
Configure simulation parameters using real production data, including concurrency levels, think times, and transaction mix, to improve predictive validity.
Model the impact of architectural changes (e.g., caching layers, database sharding) on end-to-end response times before implementation.
Run what-if scenarios for infrastructure consolidation projects, evaluating risks of resource contention under projected load increases.
Validate model outputs against actual performance during controlled load tests or production change windows.
Document model assumptions and limitations to manage stakeholder expectations when forecasting beyond historical patterns.

Module 4: Monitoring, Alerting, and Threshold Management

Configure dynamic thresholds using statistical process control methods instead of static percentages to reduce false alerts during normal usage fluctuations.
Define alert escalation paths that differentiate between capacity warnings (e.g., sustained 80% CPU) and immediate risks (e.g., disk space below 5%).
Integrate capacity alerts with incident and problem management systems to trigger formal investigations when thresholds are breached repeatedly.
Balance monitoring granularity with system overhead by limiting deep-dive collection to critical services and peak periods.
Adjust monitoring scope when services migrate to managed cloud platforms, relying on provider metrics while retaining key end-to-end transaction visibility.
Regularly review and retire obsolete thresholds tied to decommissioned services or outdated performance assumptions.

Module 5: Resource Optimization and Right-Sizing Initiatives

Initiate virtual machine right-sizing projects by analyzing CPU, memory, and storage utilization trends, balancing performance risk with cost savings.
Negotiate reserved instance commitments in public cloud based on 12-month utilization forecasts, accepting financial penalties for early termination if workloads shift.
Implement automated scaling policies for stateless applications, defining cooldown periods and step adjustments to prevent thrashing.
Identify underutilized database instances for consolidation, assessing application compatibility and licensing constraints before migration.
Enforce resource quotas in shared environments (e.g., development, test) to prevent capacity hoarding and ensure fair allocation.
Document optimization outcomes and residual risks to support audit requirements and inform future investment decisions.

Module 6: Capacity Governance and Cross-Functional Coordination

Establish a capacity review board with representation from infrastructure, application, and business teams to approve major capacity changes.
Define ownership for capacity data accuracy, assigning responsibility to system owners who control configuration and usage patterns.
Integrate capacity sign-off into the change advisory board (CAB) process for high-risk infrastructure modifications.
Resolve conflicts between application teams over shared resource allocation using documented service priorities and SLA tiers.
Enforce capacity documentation standards in the configuration management database (CMDB), including update frequency and audit procedures.
Coordinate with security and compliance teams when capacity changes affect audit log retention or monitoring coverage.

Module 7: Demand Management and User Behavior Influence

Implement reporting throttles or scheduled access windows to manage uncontrolled query loads from business intelligence tools.
Design user incentives (e.g., off-peak batch processing credits) to shift non-critical workloads away from peak business hours.
Collaborate with application owners to enforce input validation and pagination limits, reducing backend strain from inefficient queries.
Communicate upcoming capacity constraints to business units in advance, enabling them to adjust project timelines or usage patterns.
Evaluate the impact of self-service provisioning on demand volatility and implement approval workflows for high-resource requests.
Monitor the effectiveness of demand-shaping initiatives through before-and-after utilization comparisons and user feedback loops.

Module 8: Continuous Improvement and Feedback Integration

Conduct post-incident reviews for capacity-related outages to identify gaps in modeling, monitoring, or response procedures.
Update capacity models quarterly using actual performance data, adjusting growth rates and workload profiles based on observed trends.
Integrate capacity metrics into service reviews with customers, using data to justify infrastructure investments or usage policy changes.
Refine forecasting algorithms based on prediction accuracy over time, introducing new variables such as application version changes or user growth rates.
Standardize capacity reporting formats across services to enable benchmarking and cross-team learning.
Feed capacity constraints into the service design phase of new projects, ensuring scalability requirements are addressed during architecture decisions.