Description

This curriculum spans the technical and operational rigor of a multi-workshop program on IT resource governance, comparable to an internal capability build for cloud and data center optimisation across capacity planning, virtualisation, cost control, and automation.

Module 1: Capacity Planning and Demand Forecasting

Selecting time-series forecasting models based on historical usage patterns and business seasonality for server and storage capacity projections.
Integrating application release calendars into capacity models to anticipate resource spikes from new feature deployments.
Defining thresholds for over-provisioning versus risk of performance degradation during peak load events.
Allocating buffer capacity for disaster recovery workloads without compromising production service level agreements.
Reconciling conflicting capacity requests from development teams with long-term infrastructure cost constraints.
Implementing automated scaling triggers based on predictive analytics rather than reactive monitoring thresholds.

Module 2: Virtualization and Container Orchestration Efficiency

Determining optimal VM density per host while managing noisy neighbor risks and I/O contention.
Setting CPU and memory reservations and limits in Kubernetes to prevent resource starvation across microservices.
Choosing between VMs and containers based on workload isolation, startup latency, and security requirements.
Right-sizing persistent volumes in containerized environments to avoid underutilized storage claims.
Configuring node affinity and taints to align workloads with hardware-specific capabilities (e.g., GPU, high memory).
Managing cluster autoscaling policies to balance rapid scaling needs against cloud provider billing granularity.

Module 3: Cloud Resource Optimization and Cost Governance

Implementing tagging standards across cloud resources to enable accurate cost allocation by department and project.
Evaluating reserved instance versus spot instance usage based on workload criticality and uptime requirements.
Enforcing budget alerts and automated shutdown policies for non-production environments during off-hours.
Negotiating enterprise discount plans with cloud providers while maintaining architectural flexibility.
Identifying and decommissioning orphaned resources such as unattached disks and idle load balancers.
Designing multi-cloud workload placement strategies to avoid vendor lock-in while managing data transfer costs.

Module 4: Monitoring, Metrics, and Performance Baselines

Selecting key performance indicators (KPIs) for resource utilization that align with business service metrics, not just infrastructure stats.
Configuring sampling rates for telemetry data to balance monitoring accuracy with storage and processing overhead.
Establishing dynamic baselines for CPU, memory, and disk I/O to detect anomalies without excessive false positives.
Correlating application performance data with infrastructure metrics to isolate bottlenecks across service tiers.
Managing retention policies for performance data based on compliance requirements and troubleshooting needs.
Integrating synthetic transaction monitoring to validate resource adequacy under simulated user load.

Module 5: Storage Tiering and Data Lifecycle Management

Classifying data by access frequency and business criticality to assign appropriate storage tiers (SSD, HDD, object).
Implementing automated data migration policies between storage classes based on last access date and file type.
Designing snapshot schedules that minimize performance impact while meeting recovery point objectives.
Assessing the cost-benefit of data deduplication and compression in backup and archival systems.
Enforcing data retention rules in alignment with legal holds and regulatory requirements.
Planning for storage reclamation after application decommissioning to recover stranded capacity.

Module 6: Power and Thermal Management in Data Centers

Mapping server utilization to power draw metrics using rack-level PDUs and environmental sensors.
Consolidating low-utilization workloads to enable physical server decommissioning and reduce power consumption.
Adjusting cooling setpoints based on real-time rack inlet temperatures and ASHRAE guidelines.
Implementing dynamic fan speed controls to balance cooling efficiency with acoustic and power constraints.
Planning for hot aisle/cold aisle containment retrofits in existing data center layouts.
Evaluating the impact of high-density compute deployments on power distribution unit (PDU) capacity.

Module 7: Governance, Chargeback, and Accountability Models

Designing chargeback or showback models that reflect actual resource consumption without discouraging innovation.
Assigning cost centers and owners to cloud accounts and projects to enforce financial accountability.
Resolving disputes over resource allocation when business units exceed approved budgets.
Integrating resource utilization reports into executive reviews to drive capacity investment decisions.
Implementing approval workflows for provisioning high-cost resources such as large database instances.
Auditing access controls to prevent unauthorized resource creation that bypasses governance policies.

Module 8: Automation and Policy-Driven Resource Management

Developing infrastructure-as-code templates that enforce resource sizing standards and tagging requirements.
Creating automated remediation scripts for shutting down or resizing underutilized instances.
Defining policy rules in configuration management tools to detect and report non-compliant resource configurations.
Orchestrating batch job scheduling to leverage off-peak capacity and avoid interference with interactive workloads.
Integrating resource optimization tools with incident management systems to reduce false alerts from capacity issues.
Testing rollback procedures for automated scaling actions that inadvertently impact application performance.