技能 职场通用 SIGMETRICS论文选题与投稿指南

SIGMETRICS论文选题与投稿指南

v20260724
sigmetrics-topic-selection
本指南帮助研究人员确定其计算机系统性能评估研究的最佳投稿平台和子方向。它强调,合格的贡献必须是严格的性能评估结果,例如理论证明、原理性测量或有保证的学习算法,而非仅仅是系统实现或单纯的网络测量。
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概览

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.

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Category 职场通用
Name sigmetrics-topic-selection
版本 v20260724
大小 5.8KB
更新时间 2026-07-29
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