Description

This curriculum spans the technical and organisational complexity of a multi-workshop cloud performance optimisation program, addressing the interplay between architecture, operations, and governance seen in large-scale cloud adoption projects.

Module 1: Performance Requirements Definition and Baseline Establishment

Selecting transactional vs. batch performance benchmarks based on business SLAs for order processing systems.
Instrumenting production workloads to capture latency, throughput, and error rate baselines before migration.
Defining acceptable performance thresholds for peak vs. off-peak operations in multi-region applications.
Mapping legacy system performance profiles to cloud-native service tiers (e.g., matching on-prem database IOPS to provisioned cloud storage).
Documenting non-functional requirements for third-party audit and compliance validation during cloud transition.
Establishing performance budget allocations per microservice to prevent resource contention in shared environments.

Module 2: Cloud Architecture Design for Performance Scalability

Choosing between auto-scaling groups and container orchestration (e.g., Kubernetes HPA) based on application startup latency sensitivity.
Implementing caching layers (e.g., Redis or ElastiCache) with cache-aside vs. read-through strategies for high-frequency data access.
Designing asynchronous communication patterns using message queues (e.g., SQS, Pub/Sub) to decouple performance-critical components.
Selecting regional vs. multi-regional deployment architectures based on data residency laws and user proximity requirements.
Configuring CDN edge locations with cache invalidation policies aligned to content update frequency.
Integrating database read replicas with connection pooling to reduce primary instance load during reporting workloads.

Module 3: Migration Strategy and Performance Risk Mitigation

Executing pilot migrations in non-production environments to validate performance parity with legacy systems.
Choosing rehosting vs. refactoring based on application statefulness and real-time processing dependencies.
Implementing blue-green deployment with weighted routing to monitor performance impact during cutover.
Planning data migration batches to minimize network congestion and avoid peak business hours.
Using database migration tools with throttling controls to prevent source system degradation during replication.
Configuring DNS TTL values in advance to accelerate failover during migration rollback scenarios.

Module 4: Monitoring, Observability, and Performance Diagnostics

Deploying distributed tracing across microservices to identify latency bottlenecks in request chains.
Configuring synthetic transaction monitoring to simulate user workflows across geographic regions.
Setting up alert thresholds for CPU steal time and memory ballooning in virtualized cloud instances.
Correlating application logs with infrastructure metrics to isolate root causes of performance degradation.
Implementing custom metrics collection for business-critical operations not captured by default monitoring tools.
Managing log retention policies to balance forensic analysis needs with storage cost constraints.

Module 5: Resource Optimization and Cost-Performance Trade-offs

Selecting reserved instances vs. spot instances based on application fault tolerance and uptime requirements.
Right-sizing VMs using performance telemetry to eliminate over-provisioning without risking SLA breaches.
Implementing vertical pod autoscaling in Kubernetes while managing application restart frequency.
Evaluating cold start latency of serverless functions against always-on container workloads for real-time APIs.
Using storage tiering (e.g., S3 Standard vs. Glacier) with lifecycle policies based on access patterns.
Optimizing database indexing strategies to reduce query time while managing write performance overhead.

Module 6: Security, Compliance, and Performance Interdependencies

Assessing encryption overhead (e.g., TLS 1.3 vs. 1.2) on API response times in high-throughput systems.
Configuring WAF rules to mitigate DDoS attacks without introducing unacceptable request processing latency.
Implementing just-in-time access controls while maintaining session persistence for performance-sensitive applications.
Integrating secrets management (e.g., HashiCorp Vault) with minimal impact on application startup time.
Validating audit logging mechanisms do not saturate disk I/O on transaction-heavy database instances.
Negotiating compliance audit frequency to reduce performance impact of continuous monitoring scans.

Module 7: Operational Governance and Performance Lifecycle Management

Establishing change advisory board (CAB) review criteria for performance-impacting infrastructure changes.
Defining rollback procedures for deployments that exceed latency or error rate thresholds.
Conducting quarterly performance regression testing after cloud platform updates or patches.
Enforcing tagging standards for resources to enable accurate performance and cost attribution by team.
Managing technical debt by scheduling refactoring windows for performance-critical legacy components.
Integrating performance KPIs into incident response playbooks for faster resolution of outages.

Module 8: Cross-Functional Collaboration and Stakeholder Alignment

Facilitating joint performance testing sessions between development, operations, and business units.
Translating technical performance metrics into business impact terms for executive reporting.
Resolving conflicts between development velocity and production stability in CI/CD pipeline design.
Coordinating capacity planning cycles with finance teams for budget-constrained scaling initiatives.
Documenting service dependencies for incident management during cross-team outages.
Aligning DR drill schedules with business operations to minimize disruption to performance-sensitive workflows.