Description

This curriculum spans the technical and operational rigor of a multi-workshop capacity planning engagement, covering the same workload analysis, performance modeling, and governance practices applied in enterprise application management programs.

Module 1: Workload Characterization and Demand Forecasting

Decide between time-series forecasting models (e.g., ARIMA vs. exponential smoothing) based on historical data stability and seasonality patterns in transaction volume.
Segment application workloads by user type (e.g., internal staff, external customers, batch processes) to isolate demand drivers and improve forecast accuracy.
Implement data collection pipelines from application logs and monitoring tools to capture transaction rates, session durations, and peak concurrency.
Balance the frequency of forecast updates against operational overhead—daily reforecasting may improve accuracy but increases maintenance burden.
Integrate business planning inputs (e.g., product launches, marketing campaigns) into demand models to account for non-technical demand spikes.
Establish thresholds for forecast deviation that trigger capacity review meetings, avoiding overreaction to minor variances.

Module 2: Performance Baseline Establishment

Define service-level objectives (SLOs) for response time and throughput under normal and peak loads, aligned with business-critical transaction types.
Conduct controlled load testing to measure system behavior at increasing concurrency levels and identify performance inflection points.
Select representative transaction profiles for baseline testing, excluding outliers that skew resource consumption metrics.
Determine the appropriate duration for baseline measurement windows to capture diurnal and weekly usage cycles.
Document hardware, OS, and middleware configurations used during baseline tests to enable reproducibility across environments.
Update performance baselines after major application releases or infrastructure changes to maintain relevance.

Module 3: Resource Modeling and Sizing

Choose between vertical and horizontal scaling models based on application statefulness, licensing constraints, and cloud provider limitations.
Estimate memory requirements per concurrent user by analyzing heap usage patterns and garbage collection behavior in JVM-based applications.
Model database I/O requirements using query execution plans and disk queue length metrics under simulated load.
Allocate CPU headroom (e.g., 20–30%) above peak measured utilization to accommodate burst traffic and background processes.
Size network bandwidth based on average and peak request/response payloads, including overhead from encryption and protocol headers.
Account for storage growth from application logs, audit trails, and temporary data when projecting disk capacity over a 12-month horizon.

Module 4: Capacity Monitoring and Alerting

Configure monitoring thresholds using dynamic baselines rather than static percentages to reduce false alerts during normal usage fluctuations.
Correlate infrastructure metrics (e.g., CPU, memory) with application-level indicators (e.g., queue depth, error rates) to detect bottlenecks accurately.
Implement synthetic transaction monitoring to detect degradation in user-facing performance before real users are impacted.
Design alerting rules that escalate based on duration and severity, avoiding notification fatigue from transient spikes.
Exclude maintenance windows and scheduled batch jobs from capacity alerts to prevent operational noise.
Standardize metric collection intervals (e.g., 1-minute vs. 5-minute) across monitoring tools to ensure consistency in trend analysis.

Module 5: Scalability Strategy and Architecture

Decide whether to implement auto-scaling groups or Kubernetes horizontal pod autoscalers based on application portability and orchestration maturity.
Design stateless application tiers to enable seamless horizontal scaling, requiring externalization of session data to Redis or similar stores.
Implement read replicas for databases to offload reporting queries, balancing replication lag against query freshness requirements.
Partition monolithic applications into microservices only when independent scaling requirements justify the operational complexity.
Configure connection pooling parameters (e.g., max pool size, timeout) to prevent resource exhaustion under high concurrency.
Validate failover mechanisms during scaling events to ensure availability when nodes are added or removed.

Module 6: Cost and Utilization Optimization

Compare reserved instance pricing against on-demand usage patterns to determine break-even points for long-term commitments.
Right-size overprovisioned instances by analyzing sustained utilization trends over 30-day periods, avoiding performance risk.
Implement scheduled start/stop policies for non-production environments, balancing developer convenience with cost savings.
Use spot instances for batch processing workloads while designing fault tolerance for instance termination.
Track per-application resource consumption using tagging strategies to enable chargeback or showback reporting.
Balance energy efficiency and performance density when selecting hardware generations in private data centers.

Module 7: Capacity Governance and Change Control

Require capacity impact assessments for all change requests involving new features, integrations, or data migrations.
Define ownership roles for capacity reviews—assigning responsibility to application owners, infrastructure leads, and DBAs.
Integrate capacity checkpoints into the CI/CD pipeline to block deployments that exceed predefined resource thresholds.
Document capacity decisions in configuration management databases (CMDB) to support audit and incident investigations.
Establish review cycles for capacity plans aligned with fiscal planning and infrastructure refresh schedules.
Enforce consistency in naming and tagging conventions across cloud resources to maintain accurate inventory and reporting.

Module 8: Incident Response and Capacity Remediation

Classify capacity incidents by severity (e.g., degraded performance vs. service outage) to determine response timelines and escalation paths.
Activate pre-approved emergency scaling procedures, including temporary instance upgrades or cache invalidation, during outages.
Conduct post-incident reviews to distinguish between capacity shortfalls and software defects as root causes of performance degradation.
Update capacity models based on actual incident data to improve future forecasting accuracy.
Implement circuit breakers and rate limiting to protect backend systems during unexpected traffic surges.
Archive diagnostic data (e.g., thread dumps, network traces) from capacity incidents for use in training and tool refinement.