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socc-experiments
GitHub用于SoCC论文实验设计与审计，强调实测而非仿真，关注尾延迟与成本，要求公平基线、可扩展性及可复现性。
触发场景
设计或审计ACM SoCC会议的系统评估实验
需要验证云系统声明的真实性与证据匹配度
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -g -y
SKILL.md
Frontmatter
{
"name": "socc-experiments",
"description": "Use when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim."
}
SoCC Experiments
Use this before submission when the evaluation is not yet locked. SoCC reviewers come from both SIGMOD and SIGOPS, and the evaluation is where a good cloud idea is won or lost. The organizing principle is measured evidence proportional to the claim — the study must test the thing the paper asserts, on a system and workloads a skeptic from either community would accept, with tail latency and cost reported, not just the mean.
Evaluation audit
- Prefer a real or realistic deployment over simulation. A cloud claim about a system needs the system running on a testbed or cluster; simulation alone invites the "does it hold on real hardware?" reject. Where full scale is impossible, run a faithful scaled deployment and say so.
- Use production or representative workloads/traces, with stated provenance and selection, and release the replay harness. Synthetic-only microbenchmarks invite the "does this reflect real load?" objection.
- Report tail and cost, not just averages. p95/p99 (p99.9 where it bites), the latency distribution, and a concrete cost model (instance-seconds or $) are the outcomes operators are billed against.
- Choose fair baselines, including the strongest prior system and a simple-but-reasonable alternative, each tuned with a documented, equal budget. An untuned baseline is a scored weakness.
- Show scale and multi-tenancy behavior: how the result holds as nodes, tenants, or load grow, and where the bottleneck appears — SoCC papers live at scale.
- Make the measurement pipeline reproducible (see the code block) so the evaluation reproduces rather than re-samples.
- Design limits in, not on: know before you run which provider, region, and regime limits the study will have, and instrument to bound them.
Claim-to-evidence design table
| Cloud claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "System raises throughput at scale" | Throughput across node counts on a real testbed vs. tuned baseline | "Simulated / small-scale only" |
| "Holds tail latency under bursts" | p99 distribution under bursty replayed load | "Only mean latency reported" |
| "Cuts cost" | Instance-seconds or $ under a stated pricing model | "Cost asserted, never measured" |
| "Fair under multi-tenancy" | Per-tenant SLO attainment with contending workloads | "Single-tenant microbenchmark" |
| "The new mechanism adds the value" | Ablation isolating the mechanism vs. a simpler policy | "Mechanism's marginal value never isolated" |
Tail, cost, and scale floor
[Tail] report percentiles and the distribution, not just the mean; state the SLO target
[Cost] define the pricing model; report the cost metric so a reader can recompute the saving
[Scale] sweep nodes/tenants/load; identify the bottleneck where the result degrades
[Runs] report the number of runs and the variance for any measured metric
[Compute] state the testbed actually used (nodes, instance types, kernel), not vague feasibility
Workload and trace provenance floor
- Pin commit SHAs and record trace extraction dates; archive the replayed trace or a faithful generator, not just a pointer or a query.
- State inclusion/exclusion criteria and the resulting workload, with the filtering/replay scripts in the artifact.
- Report how the trace maps onto the testbed (scaling factors, time compression) so a reader can judge realism.
Vignette: evaluating a storage QoS mechanism
Suppose the paper claims a scheduler enforces latency SLOs on shared storage better than the prior system. The matching plan: run both on a real storage testbed with a mix of latency-sensitive and batch tenants replayed from a representative workload; tune both with an equal, documented budget; report per-tenant p99 attainment and aggregate throughput with variance; show behavior as tenants and load scale and name the bottleneck; report the provisioning cost of each; and state external-validity limits (storage backend, workload mix) — every number traceable to a logged run in the artifact.
Statistical and measurement reporting floor
- Latency distributions and tail percentiles for every comparison, not point estimates.
- Number of runs and the source of variance for any measured metric.
- A stated cost model behind any economic claim; the compute actually consumed.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: testbed/workload/metric incl. tail+cost>
[Deployment realism] <real testbed / scaled deployment / simulation-only>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Tail+cost+scale] <percentiles + cost model + scaling behavior reported? yes/no>
[Provenance] <trace SHAs+dates, replay harness, testbed described? yes/no>
[Decision-critical next run] <one experiment or measurement extension>
版本历史
- 9f86f09 当前 2026-07-19 17:41
