# Sigmetrics Topic Selection > 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). - Skill: `thedixitjain/sigmetrics-topic-selection` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/sigmetrics-topic-selection` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/sigmetrics-topic-selection/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/sigmetrics-topic-selection --- # 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 ```text [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 ```text [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 / 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. --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `SIGMETRICS-Skills/skills/sigmetrics-topic-selection/SKILL.md`