osdi-experiments

name	osdi-experiments
description	Use when designing or auditing the evaluation of an OSDI submission — choosing mature baselines and realistic workloads, structuring the section around research questions, measuring scalability and tail behavior, quantifying the design's costs, and fitting the evidence into the 12-page reviewed body.

OSDI Experiments

Design the evaluation as the paper's proof obligation. The page constraints referenced here are OSDI '26 rules (12 reviewed pages, no appendices at submission — verified 2026-07-08); the evidence standards are the durable expectations of systems PCs.

Research questions first

Write the evaluation's research questions before running anything, and derive the experiment set from them. Every OSDI evaluation ultimately answers versions of:

1. Does the idea work end to end? — the headline comparison on a realistic workload against the strongest baseline.
2. Where does the benefit come from? — component breakdown attributing the win to the named design idea rather than to incidental engineering.
3. What does it cost? — the overheads the design admits (memory, write amplification, CPU, complexity), measured, not estimated.
4. When does it break? — scalability limits, adversarial workloads, failure and recovery behavior.

An evaluation organized as RQ1–RQ4 with one experiment cluster each reads as an argument; a tour of every benchmark you happened to run reads as padding, which the OSDI '26 CFP explicitly invites reviewers to down-rank.

Baselines that fight back

The baseline question decides more OSDI reviews than any other. Standards:

• Compare against the strongest deployed or published system for the problem, at its tuned best — not a strawman configuration. Reviewers who built the baseline will recognize a sandbagged setup instantly.
• If the nearest competitor is unavailable, re-implement from its paper and say so, with the re-implementation validated against published numbers where possible.
• Include the do-nothing baseline where it is honest: sometimes the existing system plus more hardware is the real alternative, and the paper is stronger for pricing it.
• Version, configuration, and tuning of every baseline belong in the paper. Under the no-appendix rule this is body text — budget for it.

Workload realism

Workload tier	Role in the argument	Trap
Microbenchmarks	Isolate a mechanism; explain why the end-to-end effect exists	As the only evidence: workshop-grade
Standard suites (e.g., YCSB-class)	Comparability with prior papers	Defaults nobody runs in production
Trace-driven / production-derived	The claim's load-bearing evidence	Provenance undocumented (see osdi-reproducibility)
Adversarial / stress	Answers RQ4 honestly	Omitted, leaving reviewers to imagine worse

Systems reviewers read workload sections looking for the flattering-choice smell: the one skew setting, working-set size, or thread count where the design shines. Sweep the parameter, show the crossover point, and say where the baseline wins — a visible crossover is credibility, not weakness.

Measurement discipline

• Report distributions, not averages, wherever latency matters: medians and p99s diverge exactly where systems papers live. State run counts and variance.
• Separate warm and cold behavior; state measurement windows and what was discarded.
• Attribute wins: a profile or counter-level breakdown connecting the speedup to the mechanism ("the win disappears when we disable per-tenant ordering") beats any additional benchmark.
• Failure experiments need injected faults with stated injection method and timing, not prose about what recovery "would" do.

Experiment matrix skeleton (freeze ~8 weeks before the December deadline):
RQ | workload (tier + provenance) | baselines (version, tuning) | metric
   | scale points | runs x seeds | expected figure/table | status
Freeze the matrix, then let deadline pressure cut rows, never redefine them —
redefinition under pressure is how flattering choices happen.

Fitting evidence into 12 pages

With no appendix at submission, the evaluation must be self-sufficient and compact:

• One figure or table per research question, each with a caption stating its takeaway.
• Cut experiments that do not serve an RQ, even if they took weeks — the accepted-paper 14-page budget (plus appendices) can resurrect them later (osdi-camera-ready).
• Keep the setup paragraph brutal: hardware, topology, versions, workloads, runs — in one place, once (osdi-reproducibility owns the full ledger).

Reporting grid

Match each metric class to its honest presentation before making figures:

Metric class	Report as	Not as
Throughput	Curve vs offered load, to saturation	Single peak number
Latency	Median + p99 (p999 if claimed), distribution across runs	Mean ± nothing
Recovery/failover	Timeline from fault injection, per scale point	"Fast recovery" prose
Overhead (the design's cost)	Same rigor as the win, same table	Footnote estimate
Scalability	Efficiency vs ideal at each point	"Near-linear" unquantified

One convention repays its cost: keep the baseline's color/marker identical across every figure, so the skim (osdi-review-process) reads the comparison correctly without consulting legends.

Review-time exposure

The evaluation objections you cannot rebut (no response period in 2026) are the predictable ones: weak baseline, unrealistic workload, missing cost measurement, and average-only latency. Audit for exactly these four before submission; each unaddressed one is a review point conceded silently.

Output format

[RQ coverage] RQ1-4 each mapped to experiments? gaps: <list>
[Baseline verdict] strongest opponent present + tuned? <one-line judgment>
[Workload realism] tiers present; flattering-choice risks: <list>
[Cost honesty] design's costs measured? <which, where>
[Tail discipline] distributions + variance reported? <yes/no + fix>
[Page fit] evaluation length vs 12-page budget; cut candidates: <list>