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sigmetrics-related-work

Use when positioning an ACM SIGMETRICS submission against the performance-evaluation literature across SIGMETRICS/POMACS, Performance Evaluation, QU…

不碰外部(只输出文字)无严重或高危命中brycewang-stanford/Awesome-Journal-Skills

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技能内容

SIGMETRICS Related Work

Use this to audit novelty and eligibility. SIGMETRICS reviewers are close to the

performance-evaluation literature and expect to see where your paper sits relative to the nearest

prior model, bound, or measurement — stated as a delta, not a list. Reopen the current call for

the simultaneous-submission and prior-publication rules (a paper under one-shot revision counts as

under submission) before advising authors.

Positioning checks

  • Separate the analytic/measurement novelty from the engineering effort. What is new: a tighter

bound, a more general model, a policy that provably beats a known one, a measurement of a system

nobody had characterized, or a learning algorithm with a new guarantee?

  • Cover the performance-evaluation lanes (see the table), not just the papers nearest your

method. A bibliography missing the obvious queueing-theory predecessor or the prior measurement of

the same system reads as unaware.

  • Write delta-first. Each closely related paper gets one sentence naming what it did and one

naming what you do differently — a tighter bound, a weaker assumption, a broader policy class, a

larger/newer measurement — not a summary.

  • Preserve double-anonymity. Cite your own prior work in the third person and never link

reviewers to an identity-revealing preprint, system page, or repository (Operational Systems Track

excepted).

  • Declare overlap with any prior conference/workshop version or concurrent submission; do not

re-submit archival work as new.

Performance-evaluation literature lanes

| Lane | Typical venues | What SIGMETRICS reviewers check |

|---|---|---|

| Core performance evaluation | SIGMETRICS/POMACS, Performance Evaluation, QUESTA | Whether the nearest model/bound/measurement is compared or distinguished |

| Systems (when you claim a systems payoff) | NSDI, OSDI, SIGCOMM, ATC | Whether the system you improve/measure is credited and fairly baselined |

| Measurement | IMC, PAM, INFOCOM | Whether prior measurements of the same system/workload are engaged |

| Learning (Learning track) | NeurIPS, ICML, COLT | Whether the learning-theoretic predecessor (regret bounds, algorithms) is cited to its origin |

| Networking/queueing journals | IEEE/ACM TON, QUESTA, Stochastic Models | Whether deeper journal-length analyses of the model are engaged |

A bibliography that cites only your own subarea tells a reviewer the delta may be smaller than

claimed; one that reaches the neighboring theory, systems, and measurement venues signals command of

the field.

Delta-first positioning vignette

Suppose the paper proves a tail-latency bound for a rank-based scheduler. Its nearest neighbors: a

prior analysis of a single age-based policy (one policy, mean latency), a general scheduling

framework (broad class, but no tail bound), and a measurement study of the target system (data, no

policy analysis). The novelty sentence should name all three contrasts — a tail bound where the

single-policy analysis gave only mean, a provable tail guarantee where the framework gave none,

and a policy with analysis where the measurement gave only characterization.

Concurrent and prior-version judgment calls

[Concurrent arXiv work]   cite neutrally, state the technical difference (tighter bound? weaker
                          assumption? newer measurement?), avoid unverifiable priority claims;
                          keep the citation double-anonymous
[Your workshop version]   usually non-archival and citable, but confirm against the current call
                          wording and phrase so anonymity survives
[Prior short version]     declare the overlap and state what the full paper adds (proofs, validation)
[Paper under one-shot revision] it is under submission to SIGMETRICS -- do not submit it elsewhere
                          before withdrawing

Eligibility red flags

  • Substantial text/result overlap with a published paper by the same authors (self-plagiarism risk).
  • A "new" analysis that re-derives a known bound without a tighter result or weaker assumption.
  • Citations exclusively to non-performance-evaluation venues, signaling the paper may be a systems

or learning paper rerouted without reframing.

Output format

[Eligibility] clear / needs declaration / risky
[Lanes covered] <performance-eval / systems / measurement / learning / journals>
[Nearest 3 works] <work -> one-line delta (tighter bound / weaker assumption / broader class / newer data)>
[Archival-overlap risk] <none / declare: what>
[Novelty sentence] <SIGMETRICS-ready contribution contrast against the nearest prior work>

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