Description

This curriculum spans the technical and operational rigor of a multi-workshop capacity advisory engagement, covering modeling, monitoring, and governance practices used in enterprise infrastructure and cloud environments.

Module 1: Foundations of Capacity Planning and Demand Forecasting

Selecting between time-series forecasting models (e.g., exponential smoothing vs. ARIMA) based on data availability and historical volatility in resource consumption.
Defining service-level thresholds for performance metrics (e.g., CPU utilization at 75% sustained) to trigger capacity review cycles.
Integrating business workload calendars (e.g., fiscal closing, marketing campaigns) into seasonal demand models.
Deciding whether to use peak, average, or percentile-based baselines (e.g., 95th percentile) for capacity projections.
Establishing data collection intervals (e.g., 5-minute vs. 15-minute polling) to balance monitoring overhead with forecasting accuracy.
Documenting assumptions in growth models (e.g., 10% YoY increase) and defining triggers for model revalidation.

Module 2: Infrastructure Capacity Modeling and Simulation

Configuring synthetic workload generators (e.g., JMeter, LoadRunner) to mirror real user transaction patterns across tiers.
Mapping application transaction paths to underlying infrastructure components for end-to-end capacity tracing.
Choosing between analytical modeling (e.g., queuing theory) and simulation-based approaches based on system complexity.
Calibrating simulation models using actual performance data from production environments to reduce variance.
Modeling the impact of virtualization overhead (e.g., hypervisor CPU steal time) on effective capacity.
Assessing the scalability limits of stateful vs. stateless components under increasing concurrency.

Module 3: Cloud and Hybrid Resource Sizing Strategies

Comparing reserved instances vs. on-demand vs. spot instances based on workload predictability and cost-risk tolerance.
Designing auto-scaling policies that incorporate both utilization thresholds and predictive scaling triggers.
Accounting for network egress costs and bandwidth constraints when projecting cloud capacity needs.
Defining scaling boundaries to prevent runaway provisioning due to monitoring anomalies or application bugs.
Aligning cloud burst strategies with on-premises capacity limits and data residency requirements.
Implementing tagging and allocation models to attribute cloud spend and capacity usage by business unit.

Module 4: Database and Storage Capacity Engineering

Estimating growth in transaction logs and tempdb usage under peak OLTP workloads for buffer planning.
Projecting storage IOPS requirements based on query patterns and indexing strategies.
Planning for index fragmentation and its impact on storage overhead and performance over time.
Designing retention and archiving policies for historical data to control database size growth.
Assessing the impact of compression (row/page, backup) on storage needs and CPU utilization trade-offs.
Right-sizing SAN/NAS LUNs with consideration for thin vs. thick provisioning and over-subscription ratios.

Module 5: Capacity Monitoring and Performance Data Analysis

Selecting key performance indicators (KPIs) per tier (e.g., queue depth for storage, response time for app servers).
Configuring baselining tools to detect anomalies while filtering out scheduled batch processing spikes.
Correlating infrastructure metrics with application logs to isolate bottlenecks during contention events.
Managing retention periods for performance data based on compliance, troubleshooting, and modeling needs.
Normalizing performance data across heterogeneous environments for comparative analysis.
Validating monitoring agent overhead to ensure data collection does not skew capacity measurements.

Module 6: Capacity Governance and Change Integration

Enforcing capacity sign-off as part of the change advisory board (CAB) process for major deployments.
Defining thresholds for capacity exceptions that require formal risk acceptance by stakeholders.
Integrating capacity impact assessments into project lifecycle documentation for new applications.
Establishing ownership for capacity reviews across infrastructure, application, and business teams.
Documenting capacity assumptions in runbooks and handover materials for operational continuity.
Conducting post-incident reviews to update capacity models after unplanned resource exhaustion.

Module 7: Scalability Testing and Benchmarking

Designing load test scenarios that reflect real-world user concurrency and data volume growth.
Isolating database contention during scalability tests by controlling connection pool sizes.
Measuring diminishing returns in throughput as resources are added (e.g., identifying knee points).
Validating failover capacity by simulating node loss during peak load conditions.
Using benchmark results to negotiate SLAs with vendors or cloud providers.
Archiving test configurations and results for regression comparison across infrastructure upgrades.

Module 8: Long-Term Capacity Roadmapping and Financial Alignment

Aligning multi-year capacity forecasts with technology refresh cycles and depreciation schedules.
Presenting capacity options (scale-up vs. scale-out) with TCO implications to financial stakeholders.
Factoring in lead times for hardware procurement and data center provisioning in expansion plans.
Negotiating vendor contracts with scalability clauses to accommodate unforeseen demand spikes.
Modeling the impact of software licensing models (per-core, per-socket, subscription) on capacity decisions.
Updating capacity roadmaps quarterly based on actual consumption trends and business pivots.