Calculate burst drain time with fairness and retry reservations
Operations wants a queue-age target, but the capacity spreadsheet assumes every slot is always available for first attempts.
- Focused work estimate
- 5h + prerequisites
- Priority in the scenario
- Medium
- Engineering practice
- Queueing analysis · Capacity planning
Estimated field mix
- Performance engineering60%
- System design40%
Field percentages are editorial estimates of the ticket's engineering focus. They total 100%; they are not measured time, proficiency scores, or ownership evidence.
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Project context
A fictional analytics product lets customers schedule expensive reports at the top of the hour. The API remains available only if report work is admitted and cancelled predictably.
Setup prerequisites
- Create a local scheduler model and synthetic report jobs.
- Use fake execution providers; no customer queries or candidate code run on worker hosts.
Preceding work
Complete these dependencies, or supply their agreed outputs before taking this ticket.
- AADMIT-101 · Build a report arrival model that includes top-of-hour bursts
- AADMIT-102 · Define customer-visible report states and overload responses
- AADMIT-103 · Record the fairness decision for small and large report tenants
- AADMIT-104 · Specify transactional report admission with deterministic job identities
- AADMIT-105 · Design cancellation ownership for queued and running reports
- AADMIT-106 · Bound retries so failing reports cannot consume the entire service
Acceptance criteria
- Model concurrency, service-time classes and reserved retry capacity.
- Calculate per-tenant wait under the selected fairness rule.
- Show when queue-age targets cannot be met.
Implementation constraints
- Use hypothetical durations and an executable deterministic simulation.
Verification to include
- Simulate the declared top-of-hour burst.
- Double expensive reports and report target violations without inventing a speedup.
Deliverables
- Queue simulation and capacity worksheet
Rollout and recovery
Use the model to propose reviewed limits; validate assumptions with real isolated measurements before deployment.
Value of the work
For the engineer: Practice workload modeling, fairness and cancellation architecture.
For the team: Review controllable operating costs and predictable customer-facing overload behavior.
Evidence boundaries
Outcome Evidence: Tests, patches, and runbooks are requested deliverables. They become Outcome Evidence only through a qualified Mission and immutable Evidence IDs.
Ownership Evidence: Independent adaptation must be observed under a declared verification policy and cite immutable Evidence IDs. Completing a planning ticket establishes no Ownership Evidence.