SIGMETRICS Experiments

Use this before submission when the evidence is not yet locked. SIGMETRICS reviewers are performance-evaluation specialists; the evaluation is where a good model or measurement is won or lost. The organizing principle is evidence proportional to the claim — an analytic claim needs a proof and validation, a measurement claim needs a methodology a skeptic accepts, and a learning claim needs a guarantee, not only a benchmark score.

Evaluation audit

• Match evidence to the claim shape. A claim about a bound needs a proof plus a simulation that shows the analytic curve is right; a claim about a real system needs measured data and a documented methodology; a claim about a learner needs a regret/convergence guarantee; a claim about tail behavior needs tail metrics (p99), not means.
• State and test the modeling assumptions. Fit the arrival/service distributions to the real workload (QQ-plots, goodness-of-fit); a theorem whose M/G/1 assumption is never checked against the target system invites the "unrealistic model" reject.
• Show analysis-vs-simulation agreement. Overlay the analytic prediction on simulated measurements; if they disagree, the model or the proof is wrong, and it is better to find that yourself.
• Use real workloads/traces, sampled or selected by a stated criterion, and describe their provenance. Toy inputs invite the "does this hold on real systems?" reject.
• Choose fair baselines, including the strongest prior policy/algorithm and a simple-but-reasonable alternative, tuned with a documented, equal budget. An untuned baseline is a scored weakness.
• Report statistics for stochastic systems: confidence intervals over independent runs, the number of runs and the source of variance, warm-up/steady-state handling for simulations, and effect sizes for comparisons. A single run with no interval is not evidence.
• For learning contributions, report the guarantee (regret/convergence/sample complexity) and validate it empirically; leaderboard numbers alone route to an ML venue.

Claim-to-evidence design table

Validation floor for analytic results

[Proof]       full derivation (appendix within the reviewed pages); every case and assumption stated
[Simulation]  seeded, steady-state-aware; overlay the analytic prediction; report CIs and #runs
[Agreement]   quantify the gap between analysis and simulation; explain any discrepancy
[Robustness]  stress an assumption (heavy tails, estimation error) and bound the degradation

Provenance floor for measurement studies

• Record the trace/data source, the collection window, sanitization/anonymization, and access terms; archive the processed dataset (or document access), not just the collection script.
• State inclusion/exclusion criteria and the resulting sample, with the filtering script in the artifact.
• Report how outliers, gaps, and measurement artifacts were handled — silent inclusion skews every downstream metric.

Vignette: evaluating a scheduling policy

Suppose the paper claims a new policy provably reduces p99 latency versus a size-aware optimal. The matching plan: prove the bound under stated assumptions; fit the service-time distribution to a real trace and show the fit; simulate both policies with logged seeds and steady-state handling, overlay the analytic p99 on the simulated p99 with confidence intervals; run a trace-driven evaluation with both policies tuned equally, reporting effect sizes; and quantify the degradation under fetch-size estimation error — every number traceable to a logged run in the artifact.

Statistical reporting floor

• Confidence intervals and the number of independent runs for every stochastic measurement.
• Steady-state / warm-up handling stated for simulations.
• The compute and workload scale actually used, not vague feasibility language.

Output format

[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: proof? / validation? / measurement? / guarantee?>
[Assumption check] <assumption -> fit to real workload? goodness-of-fit shown?>
[Analysis-vs-measurement] <agreement shown with CIs? discrepancy explained? yes/no>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Decision-critical next run] <one experiment, proof case, or validation to add>