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name: sigmetrics-related-work description: Use when positioning an ACM SIGMETRICS submission against the performance-evaluation literature across SIGMETRICS/POMACS, Performance Evaluation, QUESTA, TON, and the systems/learning/measurement neighbors (NSDI/OSDI, IMC, NeurIPS/ICML), writing delta-first contrast rather than a citation catalog, keeping self-citations double-anonymous, and handling concurrent and prior-version overlap.

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>