Performance Profiling

objective

Execute performance profiling work with reproducible research, explicit controls, and deployable outputs.

workflow

1. •define end-to-end latency budget and deterministic performance targets.
2. •instrument each stage from feed ingress to order egress.
3. •optimize kernel, memory, and network path for tail-latency reduction.
4. •stress packet bursts, failovers, and capacity saturation scenarios.
5. •promote only after reproducible latency and recovery behavior is verified.

required diagnostics

• •stage-level p50, p99, and p999 latency decomposition.
• •jitter and throughput stability under sustained burst load.
• •packet-loss recovery time and replay correctness.
• •resource saturation signals before service-level breach.

risk controls

• •enforce hard latency and packet-loss service objectives.
• •enforce automatic failover and load-shedding thresholds.
• •enforce runbooks for exchange-connectivity incidents.

outputs

• •run python scripts/performance_profiling_diagnostics.py input.csv --output diagnostics.json and keep the json artifact.
• •write an implementation memo using references/performance-profiling-playbook.md with assumptions, tests, limits, and rollout plan.

resources

• •use scripts/performance_profiling_diagnostics.py for deterministic diagnostics.
• •use references/performance-profiling-playbook.md for the domain-specific checklist and delivery structure.