infocom-experiments

name	infocom-experiments
description	Use when designing or auditing IEEE INFOCOM evaluations, covering analytical results with stated and justified assumptions, simulation with a named simulator and logged seeds, testbed and measurement studies with real traffic, honest and tuned baselines, and matching evidence to the shape of each networking claim across the analysis-simulation-testbed spectrum.

INFOCOM Experiments

Use this before submission when the evaluation is not yet locked. INFOCOM reviewers span the full analysis → simulation → testbed spectrum, and the evaluation is where a good idea is won or lost — especially since there is (traditionally) no rebuttal to repair a weak comparison. The organizing principle is evidence proportional to the claim, delivered by the method the claim demands: a bound needs a proof, a design needs a fair experiment, a behavior claim needs measurement.

Evaluation audit

• Match the method to the claim. An optimality/approximation claim needs a proof under stated assumptions; a performance claim needs a fair experiment; a real-world behavior claim needs measurement on real traffic. A simulation cannot substitute for a proof, and a proof under unrealistic assumptions cannot substitute for an experiment.
• State and justify every assumption. For analytical work, list the assumptions (A1, A2, ...) and defend each; a theorem true only under an assumption no network satisfies is a scored weakness a reviewer will name — and you cannot rebut it.
• Name the simulator and pin the setup. State whether it is ns-3, OMNeT++, a custom simulator, or a testbed; report the topology, traffic model, number of runs, seeds, and confidence intervals. "A large-scale simulation" with no setup is not evidence.
• Choose honest baselines, including the strongest prior scheme and a simple-but-reasonable alternative, each tuned with a documented, equal budget. An untuned baseline is the classic INFOCOM reject.
• Report variance, not point estimates. Confidence intervals over multiple seeded runs; appropriate statistics; say what the intervals represent.
• For testbeds/measurement, describe the hardware, the trace or workload, the collection methodology, and the confounds — and bound them.

Claim-to-evidence design table

Networking claim	Matching evidence	Reject pattern avoided
"Algorithm is near-optimal"	Proof of an approximation/competitive ratio under stated assumptions	"Only shown on examples the authors chose"
"Scheme improves throughput/delay"	Seeded simulation or testbed vs. a tuned baseline, with CIs	"Untuned baseline; single run; no variance"
"Scales to large networks"	Runtime/quality across realistic topology sizes	"Only a 10-node topology tested"
"Holds in real deployments"	Testbed or trace-driven measurement with real traffic	"Synthetic traffic claimed to generalize"
"The learner adds the value"	Ablation vs. a non-ML heuristic on the same inputs	"ML component's marginal value never isolated"

Simulation and reproducibility floor

[Simulator]  name it (ns-3/OMNeT++/custom); pin the version; describe the model, not just the tool
[Topology]   real or realistic (e.g., from a topology dataset); state size and generation method
[Traffic]    the workload/trace and its source; synthetic models justified against real behavior
[Runs/seeds] multiple seeded runs; report the number and log the seeds; give confidence intervals
[Baselines]  the strongest competitor + a simple one, each tuned with a documented equal budget
[Compute]    the runtime/scale actually reached, not vague feasibility language

Analytical-work floor

• List assumptions explicitly and justify each against real network conditions.
• Give proof sketches in the body and full proofs in a tight in-budget appendix (it counts toward the nine pages) or state where the full proof lives.
• Validate the theory in simulation: show the bound is tight/loose where the analysis predicts.

Vignette: evaluating a caching/offloading policy

Suppose the paper claims an online caching policy with a competitive ratio that also beats prior heuristics. The matching plan: prove the ratio under stated assumptions; then simulate on a real request trace with multiple seeds, comparing against the strongest prior policy tuned with an equal budget and a simple LRU baseline; report hit-rate and delay with confidence intervals; show where the empirical gap matches the analytical bound; and state the regime (skewed vs. uniform demand) where the guarantee is loose — every number traceable to a logged run.

Statistical reporting floor

• Confidence intervals over seeded runs for every simulated/measured comparison; say what they represent.
• The number of runs and the source of randomness for any stochastic component.
• The topology sizes and traffic models actually used, not vague "large-scale" language.

Output format

[Evaluation readiness] strong / adequate / weak
[Claim -> method map] <claim: proof | simulation | testbed/measurement>
[Assumptions] <listed and justified? yes/no>
[Simulator/setup] <named? seeds logged? CIs reported? yes/no>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Decision-critical next run] <one experiment or proof to add before the deadline>