sigmetrics-topic-selection

Research

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).

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/SIGMETRICS-Skills/skills/sigmetrics-topic-selection/SKILL.md

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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.