Description

This curriculum spans the technical decision-making depth of a multi-workshop performance engineering engagement, addressing the same trade-offs in latency, scalability, and compliance that arise when tuning complex applications during and after cloud migration.

Module 1: Assessing Pre-Migration Application Performance Baselines

Decide which legacy system metrics (CPU, memory, I/O, response time) to capture and over what duration to establish statistically valid baselines.
Select monitoring tools compatible with both on-premises infrastructure and target cloud platforms to ensure consistent data collection.
Determine whether to include user transaction profiles or synthetic workloads during baseline measurement to reflect real-world usage.
Identify applications with performance thresholds that are non-negotiable (e.g., sub-second response times) and flag them for special handling.
Balance the overhead of deep-dive profiling against project timelines when assessing older, poorly documented systems.
Document dependencies between applications and backend services to anticipate cascading performance impacts during migration.

Module 2: Selecting Cloud Deployment Models for Performance Optimization

Evaluate whether to use single-AZ vs. multi-AZ deployments based on application tolerance for latency versus high availability requirements.
Decide between VM-based, containerized, or serverless hosting based on startup time, scaling behavior, and resource utilization patterns.
Assess the impact of data residency laws on region selection and its effect on end-user latency for global applications.
Compare provisioned vs. burstable instance types for cost-performance trade-offs in variable-load applications.
Configure placement groups or dedicated hosts when low-latency inter-instance communication is critical for tightly coupled systems.
Integrate third-party network performance benchmarks to validate cloud provider claims under expected load conditions.

Module 3: Database Migration and Query Performance Engineering

Choose between homogeneous (e.g., Oracle to Amazon RDS Oracle) and heterogeneous (e.g., Oracle to PostgreSQL) migrations based on licensing and long-term support.
Modify indexing strategies post-migration to account for differences in query optimizers and storage engines between source and target databases.
Implement connection pooling mechanisms to prevent exhaustion of database connections under auto-scaling workloads.
Decide whether to use read replicas, sharding, or caching layers to meet post-migration query latency SLAs.
Optimize bulk data transfer methods (e.g., AWS DMS vs. native export/import) based on downtime tolerance and data consistency requirements.
Adjust transaction isolation levels in cloud-hosted databases to balance consistency with throughput under concurrent access.

Module 4: Network Architecture and Latency Management

Design VPC peering or transit gateway configurations to minimize inter-service latency across distributed microservices.
Implement DNS routing policies (e.g., latency-based or geoproximity) to direct users to the nearest application instance.
Configure MTU settings and TCP window scaling to optimize throughput for high-bandwidth data transfers.
Decide whether to use content delivery networks (CDNs) for static assets based on user geographic distribution and cache hit ratios.
Monitor and mitigate the impact of noisy neighbors by analyzing packet loss and jitter on shared cloud infrastructure.
Establish service quotas and throttling rules to prevent one application from degrading network performance for others.

Module 5: Auto-Scaling and Resource Provisioning Strategies

Define custom CloudWatch or Prometheus metrics to trigger scaling actions beyond CPU and memory thresholds (e.g., queue depth, request latency).
Set cooldown periods and scaling step sizes to prevent thrashing during transient load spikes.
Use predictive scaling models when workloads follow predictable patterns (e.g., end-of-month reporting) to pre-warm resources.
Implement canary scaling to test new instance types or AMIs under production load before full rollout.
Configure right-sizing recommendations using tools like AWS Compute Optimizer, but validate findings against actual application behavior.
Balance spot instance usage with failover mechanisms to maintain performance during instance interruptions.

Module 6: Monitoring, Observability, and Feedback Loops

Deploy distributed tracing across microservices to identify latency bottlenecks in asynchronous communication paths.
Correlate infrastructure metrics with business KPIs (e.g., transaction completion rate) to assess real-world performance impact.
Define alert thresholds that minimize noise while ensuring timely detection of performance degradation.
Integrate synthetic transaction monitoring to detect performance regressions before user impact occurs.
Store and index logs in a centralized system with sufficient retention to support root cause analysis of intermittent issues.
Establish feedback loops between operations and development teams to prioritize performance debt remediation.

Module 7: Security and Compliance Constraints in Performance Design

Implement encryption at rest and in transit without degrading I/O performance beyond acceptable thresholds.
Configure firewall rules and security groups to minimize packet inspection overhead on high-throughput data pipelines.
Balance audit logging granularity with storage costs and query performance in SIEM systems.
Validate that hardware security modules (HSMs) or key management services do not introduce unacceptable cryptographic latency.
Isolate regulated workloads in dedicated environments, accepting potential performance trade-offs due to reduced resource pooling.
Test intrusion detection systems for false positives that could trigger unnecessary throttling or failover events.

Module 8: Post-Migration Optimization and Continuous Tuning

Conduct performance regression testing after cloud provider updates or infrastructure changes using production-like workloads.
Refactor stateful applications to leverage cloud-native storage services without introducing latency from remote access.
Optimize cold start times in serverless functions by adjusting memory allocation and minimizing dependency loading.
Re-evaluate CDN caching rules and TTLs based on actual content update frequency and user access patterns.
Use A/B testing to compare performance of different configuration sets (e.g., compression algorithms, TLS versions).
Establish quarterly performance reviews to reassess SLAs, update baselines, and identify emerging bottlenecks.