sigmetrics-topic-selection
GitHub辅助判断计算机性能项目是否适合投稿ACM SIGMETRICS，并指导选择具体赛道（理论、测量建模、学习或运维系统）。同时提供路由建议，将不适合的项目导向NSDI、IMC、NeurIPS等更合适的会议或期刊。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-topic-selection -g -y
SKILL.md
Frontmatter
{
"name": "sigmetrics-topic-selection",
"description": "Use when deciding whether a computer-systems performance project belongs at ACM SIGMETRICS or should be routed to IMC, SIGCOMM\/NSDI\/OSDI, INFOCOM, a learning venue (NeurIPS\/ICML), or a performance journal (Performance Evaluation\/TON\/QUESTA), and when picking the right SIGMETRICS track (Theory \/ Measurement & Applied Modeling \/ Learning \/ Operational Systems)."
}
SIGMETRICS Topic Selection
Decide the venue and track before drafting. SIGMETRICS — the ACM flagship for performance measurement, modeling, and evaluation of computer systems — rewards a rigorous performance-evaluation contribution: a stochastic/queueing model with a proven bound, a principled measurement study, or a learning-for-systems algorithm with guarantees. A technically strong paper whose real lesson is a built system (route to NSDI/OSDI), a pure network measurement (route to IMC), or a learning-theory result with no systems payoff (route to NeurIPS/COLT) is respected and then rejected as out of scope.
The routing question that matters most
The decisive question is rarely "is this about systems performance?" but "is the contribution an analyzed/measured performance result, or is it something else with performance numbers attached?" SIGMETRICS wants the why — a model, a proof, a validated methodology — not only a faster system or a bigger dataset.
Sibling-venue routing table
| Signal in your project | Better home | Why |
|---|---|---|
| A model/policy with a proven performance bound, or a principled measurement/modeling study | ACM SIGMETRICS | Its center: rigorous performance evaluation published in POMACS |
| The contribution is a built system; the design/implementation is the point | NSDI / OSDI / SIGCOMM | Systems-building venues; SIGMETRICS wants analysis, not a system artifact |
| The whole paper is network measurement (Internet, CDN, topology, traffic) | IMC | The dedicated network-measurement venue; single annual deadline |
| Networking with a systems/protocol contribution | SIGCOMM / NSDI / INFOCOM | Networking-systems scope |
| A learning-theory result with no systems performance payoff | NeurIPS / ICML / COLT | Learning venues; SIGMETRICS Learning track wants a systems angle or systems-relevant guarantees |
| A study too long/deep for 20 pages, or wanting multiple revision rounds | Performance Evaluation / TON / QUESTA | Journals with no conference page ceiling and open-ended revision |
Contribution shapes SIGMETRICS rewards
- Stochastic / queueing / scheduling theory — a model of a system's performance with a proven bound, stability condition, or optimality result, validated numerically (the SOAP lineage).
- Measurement & applied modeling — a principled measurement or simulation methodology and the characterization it yields about a real system (the Google-Play-study lineage).
- Learning for systems — an online-learning/bandit/RL/control algorithm for a systems problem, with regret/convergence/sample-complexity guarantees (the learning-to-rank lineage).
- Operational systems — a deployed system in significant real-world use, analyzed with principled measurement and metrics (the Operational Systems Track; may name the system/org).
The rigor and validation tests
Two quick tests sharpen a borderline verdict:
- Rigor test: does the contribution carry a checkable claim — a theorem, a stated-assumption bound, a measurement methodology a skeptic would accept — or only "it is faster on our setup"? If the latter, it is a systems-building paper (NSDI/OSDI), not SIGMETRICS.
- Model-swap / methodology test: if your paper leans on a learner or a specific system, ask whether the performance-evaluation lesson survives — a guarantee, a validated model, a general methodology. If the only result is a benchmark score, it is an ML or systems paper wearing a SIGMETRICS title.
Picking the track (do this at abstract registration)
- Theory: the core is a proof (queueing, scheduling, caching, algorithms, control).
- Measurement & Applied Modeling: the core is data from a real system + a methodology/model.
- Learning: the core is a learning algorithm with analysis, applied to or for systems.
- Operational Systems: the core is a deployed, in-use system; you may reveal its name/org.
Pick one; a second only for genuinely interdisciplinary work (e.g. a learning-theoretic result validated by measurement). The wrong track routes you to the wrong reviewers.
Cheap reconnaissance before committing
[Scope] scan the last few POMACS issues (dblp, ACM DL) for your subarea and track
-> several recent papers = a reviewer pool exists; none = opening or mismatch
[Rigor] does your headline claim reduce to a theorem, a validated model, or a principled
measurement? -> if not, reconsider SIGMETRICS vs. a systems venue
[Calendar] the next rolling deadline (summer/fall/winter) is ~a quarter away -> route to the
nearest honest fit rather than forcing a rushed proof/measurement
Decision procedure
[Audience] who acts differently if the claim holds? -> systems designers/operators/theorists?
[Claim type] queueing/theory / measurement / learning-with-guarantees / operational
[Rigor gate] is there a checkable performance claim (proof / validated model / methodology)?
[Sibling check] built system -> NSDI/OSDI; pure net-measurement -> IMC; learning-theory-only -> NeurIPS
[Verdict] SIGMETRICS <track> / sibling venue / performance journal, with a one-line reason
Run this before the writing skills; a wrong venue or track decision wastes every later step. When
the verdict is SIGMETRICS, continue with sigmetrics-workflow for the deadline choice and
sigmetrics-writing-style for the paper shape.
Version History
- 9f86f09 Current 2026-07-19 17:37
