Assessments and performance baselines
Measure normal and peak workload behavior, resource utilization, application response, storage latency, throughput, and network performance. Identify bottlenecks and contention across the virtual stack before defining an optimization plan and measurable success criteria.
On-premises virtual environments
Review VM sizing, CPU and memory allocation, host capacity, contention, storage paths, datastore performance, virtual networking, and cluster placement. Tune the selected platform and workload together, accounting for availability, maintenance, backup activity, and expected growth.
Performance across cloud providers
Apply performance-engineering skills across AWS, Microsoft Azure, and Google Cloud. Evaluate instance or VM families, resource sizing, storage performance, network paths, load balancing, scaling, application dependencies, and monitoring using each provider’s supported tools and architecture guidance.
Cloud-specific capabilities
For AWS, review compute choices, storage and data-access patterns, networking, and capacity behavior. For Azure, align VM and storage configuration, scaling, telemetry, and critical application flows. For Google Cloud, evaluate workload placement, compute and storage selection, service-level bottlenecks, and ongoing performance measurement.
Hybrid and multi-cloud dependencies
Trace application flows between local infrastructure and cloud services. Assess latency, bandwidth, data locality, identity dependencies, routing, and integration points to identify issues that cannot be resolved by resizing a virtual machine alone.
Validate and sustain improvements
Test changes against the baseline, track application outcomes, and balance performance with cost, resilience, and operational needs. Establish dashboards, alerts, capacity reviews, and a prioritized improvement roadmap so tuning remains effective as demand changes.
