Agent Skills
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atc-experiments
GitHub指导设计或审计ATC系统论文的评估环节，强调匹配证据与主张。要求使用真实测试床和负载，设置公平基线，结合端到端与微基准测试，并如实报告尾延迟、方差及资源成本，以应对审稿人质疑。
Trigger Scenarios
撰写ATC等系统顶会论文的实验评估部分
审计系统论文的测试方法是否公平且真实
检查是否报告了尾延迟和资源开销
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments -g -y
SKILL.md
Frontmatter
{
"name": "atc-experiments",
"description": "Use when designing or auditing the evaluation of an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — matching evidence to the claim with real testbeds, fair baselines, end-to-end plus microbenchmark results, tail-latency and variance reporting, workload realism, and honest cost accounting."
}
ATC Experiments
Match the evidence to the claim. ATC is the systems community's implementation-and-measurement venue: reviewers read for measured behavior on a real system, not asymptotics or accuracy on a dataset. In round two, 3-4 reviewers close to your subarea will open the artifact and probe whether the numbers are end-to-end, fair, and honest about cost. Design the evaluation so their first three objections are already answered.
Match evidence to claim shape
| If your claim is... | The evidence ATC expects |
|---|---|
| "Faster / lower latency" | End-to-end latency including tails (p99/p999) and throughput at a matched operating point, on a described testbed |
| "Lower overhead / cheaper" | The resource cost (CPU, memory, writes, energy) measured, at matched function — not just the headline win |
| "Scales" | Measurements across a real range of load/nodes/cores with the scaling curve and where it bends |
| "More reliable / correct" | Fault-injection or crash/recovery experiments, not just steady-state runs |
| "Useful in practice" (experience) | Production-derived workloads and lessons; what broke and what generalizes |
Real testbeds and workloads
- Describe the testbed so results are reproducible: CPU/NIC/SSD models, core counts, memory, kernel/OS versions, network topology, and any co-location. A result without its testbed is not a systems result.
- Use realistic workloads. Production-derived traces, standard benchmarks, or documented generators beat hand-picked inputs. State how the workload was obtained and why it is representative; if it is synthetic, justify the parameters.
- Warm-up and steady state. Say how you handled cold start, warm-up windows, and measurement duration — systems reviewers know where transient effects hide.
Fair baselines
- Compare against the strongest reasonable alternative, configured well (a strawman baseline is caught immediately). If you tuned your system, tune the baseline.
- Compare at a matched cost or operating point: same memory budget, same flash-write budget, same load. An unmatched comparison is the classic systems-reviewer objection.
- If no baseline exists, say so and use the unmodified system or an ablation of your own design as the reference.
End-to-end plus microbenchmarks
ATC reviewers want both:
- End-to-end results show the contribution matters for the whole system under a real workload.
- Microbenchmarks isolate the mechanism, attributing the win (or cost) to your design rather than to unrelated system effects. A paper with only end-to-end numbers cannot explain why; one with only microbenchmarks cannot show it matters.
Tails, variance, and honest reporting
- Report tail latency (p99, often p999), not just means — the tail is where systems pain lives.
- Report variance across repeated runs (multiple trials, min/max or CIs). A single run is a data point, not a result.
- Report the cost beside the gain, at the matched operating point (see
atc-writing-style). A win with an unstated cost reads as a hidden weakness. - State negative or neutral regions honestly — "where the working set fits, our policy neither helps nor hurts" builds more trust than a uniformly rosy curve.
Provenance you cannot reconstruct later
Pin these at collection time — they cannot be recovered at the deadline (see atc-reproducibility):
[Hardware] CPU/NIC/SSD models, core/memory counts, firmware where it matters
[Software] kernel/OS versions, library and compiler versions, config flags
[Workload] trace source and date, generator version and seeds, request mix
[Method] warm-up window, measurement duration, number of runs, aggregation
[Code] commit SHAs for the system and every baseline
Special cases
- Concurrency/nondeterminism: report the distribution and the scheduling/affinity settings, not a lucky run.
- Energy/power claims: name the measurement instrument and boundary (wall vs. component).
- Security/isolation claims: state the threat model and what the measurement does and does not cover.
- Experience papers: the "evaluation" is the deployment itself — scale, duration, incidents, and transferable lessons; ATC's Deployed Systems lane values this even without a new mechanism.
Output format
[Claim -> evidence] each claim mapped to the experiment that supports it; gaps flagged
[Testbed] hardware/software/workload described enough to reproduce? yes/no
[Baselines] strongest alternative, well-configured, at a matched operating point? yes/no
[Depth] end-to-end AND microbenchmarks present? tails + variance reported?
[Honesty] costs reported beside gains? neutral/negative regions stated?
[Provenance] hardware/software/workload/method/code pinned at collection time? yes/no
Version History
- 9f86f09 Current 2026-07-19 14:20
