osdi-reproducibility

GitHub

用于OSDI系统项目,在实验运行时记录硬件、配置和工作负载等溯源信息,确保论文与工件一致,支持后续的可复现性评估。

OSDI-Skills/skills/osdi-reproducibility/SKILL.md brycewang-stanford/Awesome-Journal-Skills

触发场景

构建可复现的OSDI系统项目 记录实验运行的硬件和配置溯源

安装

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill osdi-reproducibility -g -y
更多选项

非标准路径

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/OSDI-Skills/skills/osdi-reproducibility -g -y

不安装直接使用

npx skills use brycewang-stanford/Awesome-Journal-Skills@osdi-reproducibility

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill osdi-reproducibility -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "osdi-reproducibility",
    "description": "Use when building reproducibility into an OSDI systems project — recording hardware, configuration, workload, and measurement provenance while experiments run, keeping paper and artifact from drifting apart, and setting up for the post-acceptance sysartifacts evaluation and open-access scrutiny."
}

OSDI Reproducibility

Make the numbers survivable. OSDI-specific hooks below (artifact timing, badge scope, open-access exposure) are 2026-cycle facts verified 2026-07-08; the provenance discipline is venue-independent engineering.

Why the bar is high at OSDI specifically

Two venue mechanics raise the stakes beyond generic good practice:

  • USENIX proceedings are open access from day one. Every reader on the internet — including the teams whose systems you outperformed — gets the free PDF immediately and can attempt your numbers. Errors get found publicly and fast.
  • Artifact evaluation happens after acceptance (May 8, 2026, 8:59 pm PDT in the '26 cycle), when the experiments are months old. If provenance was not recorded while runs happened, the artifact reconstructs a memory, not an experiment.

Reproducibility at OSDI is therefore a recording problem during the project, not a packaging problem at the end. Packaging is osdi-artifact-evaluation's job; this skill makes packaging possible.

The provenance ledger

Maintain one machine-readable ledger, committed beside the code, updated by the run scripts themselves — never by hand after the fact:

# runs/2025-11-14-recovery-scale/ledger.yaml (written by the harness, per experiment)
experiment: recovery-vs-cluster-size      # maps to RQ in the experiment matrix
commit: 4f2c9e1 (system) / 8a11d02 (harness)
hardware: 64x c6525-25g (CloudLab), 25 GbE, NVMe model+fw recorded per node
os_kernel: Ubuntu 22.04, 5.15.0-91; mitigations=on; governor=performance
baseline_versions: replayfs v2.3.1 (tag), ckptstore rebuilt from paper (SHA)
workload: trace block-2025-w2, reconstruction script + source documented
runs: 10 per point; seeds 1..10; outliers kept, plotted as distribution
raw_output: s3://bucket/runs/2025-11-14/... (checksummed)
figures: fig7 <- plot_recovery.py @ 8a11d02 on raw_output

The last line is the anti-drift rule: every figure in the paper regenerates from checksummed raw output by a committed script. If a figure cannot name its script and input, the number it shows is unverifiable — by the AE committee and by you in June.

What systems papers must pin down

Dimension Must record Common omission that kills reruns
Hardware Node model, NIC, storage device + firmware, topology The NIC/firmware detail that made the difference
Software Kernel version + relevant knobs, dependency lockfile Sysctl and IRQ-affinity settings applied by hand
Baselines Exact version/tag, tuning applied, build flags "Default settings" that were quietly edited
Workloads Trace provenance, generation seed, licensing The preprocessing script that shaped the trace
Measurement Warmup policy, window, timer source, run counts Which runs were discarded and why
Environment Cluster sharing, power/turbo state, time of run Co-located tenants distorting tail latency

Hardware access is the honest limit of systems reproducibility: a result needing 64 specific machines will not rerun on a laptop. The discipline is disclosure plus graceful degradation — document the full testbed, and provide a scaled-down configuration that exercises every code path even if it cannot reproduce headline magnitudes.

Determinism where it is cheap, honesty where it is not

Distributed systems are not bitwise-reproducible; do not pretend otherwise. Seed what can be seeded (workload generation, placement decisions, fault-injection schedules), report distributions over repeated runs for what cannot, and state which class each reported number belongs to. A paper that says "recovery time varies ±8% run to run; we report 10-run distributions" pre-empts the reviewer who reruns and gets a different point value.

Traces, data, and the licensing wall

Workload provenance is where systems reproducibility most often dies quietly:

  • Production-derived traces usually cannot be redistributed. Decide the release posture before the evaluation depends on them: either obtain redistribution rights early, or build a documented reconstruction pipeline (statistical profile → generator → validation against the original) and treat the generator as part of the artifact.
  • Public traces still need version pinning and checksums — public archives reorganize, and "the standard trace" is not an identifier.
  • Sensitive measurements (multi-tenant clusters, user-facing services) need the anonymization step documented as code, because it changes distributions and a reproducer must know how.
  • Whatever the posture, the paper states it in one honest sentence; discovering an unreleasable dataset during artifact evaluation reads far worse than declaring it in the submission.

The shared smoke checker in ../../resources/code/README.md catches structural gaps (missing README/manifest/license) but none of the above — licensing and provenance are judgment calls only the authors can make.

Timing against the 2026 cycle

  • During experiments (autumn) — the ledger above, enforced by the harness.
  • Silent review window (Dec–Mar) — freeze the testbed image, trace archives, and environment; the '26 Call for Artifacts encouraged preparing artifacts while the paper was under consideration, and a conditional accept may demand new runs on the frozen setup (osdi-author-response).
  • After notification (Mar 26) — packaging sprint to the May 8 artifact deadline; in 2026 the badge evaluated was Artifacts Available, so permanent archiving is the floor — but a ledger-backed artifact is what makes the optional two-page Artifact Appendix in the final paper worth writing (osdi-camera-ready).

The handoff test

The standing acceptance test for all of the above: a new group member, given only the repository and the ledger, regenerates one paper figure on the scaled-down configuration without asking anyone anything. Run it quarterly and before each gate (submission, artifact deadline, final paper). Every question they are forced to ask is a missing ledger entry; every mismatch they hit is drift between paper and artifact that an AE evaluator — or a public reproducer holding the open-access PDF — would have found later, with an audience.

Output format

[Ledger] exists + harness-written? gaps: <dimensions from the table>
[Figure regeneration] all figures script+input traceable? failures: <list>
[Determinism statement] seeded vs distributional numbers classified? yes/no
[Testbed freeze] image/trace archive frozen for the review window? yes/no
[AE readiness] distance from ledger to packageable artifact: <low/med/high>

版本历史

  • 9f86f09 当前 2026-07-19 17:11

同 Skill 集合

AAAI-Skills/skills/aaai-artifact-evaluation/SKILL.md
AAAI-Skills/skills/aaai-author-response/SKILL.md
AAAI-Skills/skills/aaai-camera-ready/SKILL.md
AAAI-Skills/skills/aaai-experiments/SKILL.md
AAAI-Skills/skills/aaai-related-work/SKILL.md
AAAI-Skills/skills/aaai-reproducibility/SKILL.md
AAAI-Skills/skills/aaai-review-process/SKILL.md
AAAI-Skills/skills/aaai-submission/SKILL.md
AAAI-Skills/skills/aaai-supplementary/SKILL.md
AAAI-Skills/skills/aaai-topic-selection/SKILL.md
AAAI-Skills/skills/aaai-workflow/SKILL.md
AAAI-Skills/skills/aaai-writing-style/SKILL.md
AAMAS-Skills/skills/aamas-artifact-evaluation/SKILL.md
AAMAS-Skills/skills/aamas-author-response/SKILL.md
AAMAS-Skills/skills/aamas-camera-ready/SKILL.md
AAMAS-Skills/skills/aamas-experiments/SKILL.md
AAMAS-Skills/skills/aamas-related-work/SKILL.md
AAMAS-Skills/skills/aamas-reproducibility/SKILL.md
AAMAS-Skills/skills/aamas-review-process/SKILL.md
AAMAS-Skills/skills/aamas-submission/SKILL.md
AAMAS-Skills/skills/aamas-supplementary/SKILL.md
AAMAS-Skills/skills/aamas-topic-selection/SKILL.md
AAMAS-Skills/skills/aamas-workflow/SKILL.md
AAMAS-Skills/skills/aamas-writing-style/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-editor-strategy/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-evidence-standards/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-literature-synthesis/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-organizing-framework/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-proposal-framing/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-review-process/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-revision/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-submission/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-tables-figures/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-topic-selection/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-workflow/SKILL.md
Academy-of-Management-Annals-Skills/skills/amann-writing-style/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-contribution-framing/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-data-analysis/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-literature-positioning/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-methods/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-rebuttal/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-review-process/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-submission/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-tables-figures/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-theory-development/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-topic-selection/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-workflow/SKILL.md
Academy-of-Management-Journal-Skills/skills/amj-writing-style/SKILL.md
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元信息

文件数
0
版本
5717eab
Hash
d7dd79d3
收录时间
2026-07-19 17:11

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