atc-experiments

ATC Experiments

Match the evidence to the claim. ATC is the systems community's implementation-and-measurement venue: reviewers read for measured behavior on a real system, not asymptotics or accuracy on a dataset. In round two, 3-4 reviewers close to your subarea will open the artifact and probe whether the numbers are end-to-end, fair, and honest about cost. Design the evaluation so their first three objections are already answered.

Match evidence to claim shape

Real testbeds and workloads

• Describe the testbed so results are reproducible: CPU/NIC/SSD models, core counts, memory, kernel/OS versions, network topology, and any co-location. A result without its testbed is not a systems result.
• Use realistic workloads. Production-derived traces, standard benchmarks, or documented generators beat hand-picked inputs. State how the workload was obtained and why it is representative; if it is synthetic, justify the parameters.
• Warm-up and steady state. Say how you handled cold start, warm-up windows, and measurement duration — systems reviewers know where transient effects hide.

Fair baselines

• Compare against the strongest reasonable alternative, configured well (a strawman baseline is caught immediately). If you tuned your system, tune the baseline.
• Compare at a matched cost or operating point: same memory budget, same flash-write budget, same load. An unmatched comparison is the classic systems-reviewer objection.
• If no baseline exists, say so and use the unmodified system or an ablation of your own design as the reference.

End-to-end plus microbenchmarks

ATC reviewers want both:

• End-to-end results show the contribution matters for the whole system under a real workload.
• Microbenchmarks isolate the mechanism, attributing the win (or cost) to your design rather than to unrelated system effects. A paper with only end-to-end numbers cannot explain why; one with only microbenchmarks cannot show it matters.

Tails, variance, and honest reporting

• Report tail latency (p99, often p999), not just means — the tail is where systems pain lives.
• Report variance across repeated runs (multiple trials, min/max or CIs). A single run is a data point, not a result.
• Report the cost beside the gain, at the matched operating point (see atc-writing-style). A win with an unstated cost reads as a hidden weakness.
• State negative or neutral regions honestly — "where the working set fits, our policy neither helps nor hurts" builds more trust than a uniformly rosy curve.

Provenance you cannot reconstruct later

Pin these at collection time — they cannot be recovered at the deadline (see atc-reproducibility):

[Hardware]   CPU/NIC/SSD models, core/memory counts, firmware where it matters
[Software]   kernel/OS versions, library and compiler versions, config flags
[Workload]   trace source and date, generator version and seeds, request mix
[Method]     warm-up window, measurement duration, number of runs, aggregation
[Code]       commit SHAs for the system and every baseline

Special cases

• Concurrency/nondeterminism: report the distribution and the scheduling/affinity settings, not a lucky run.
• Energy/power claims: name the measurement instrument and boundary (wall vs. component).
• Security/isolation claims: state the threat model and what the measurement does and does not cover.
• Experience papers: the "evaluation" is the deployment itself — scale, duration, incidents, and transferable lessons; ATC's Deployed Systems lane values this even without a new mechanism.

Output format

[Claim -> evidence] each claim mapped to the experiment that supports it; gaps flagged
[Testbed] hardware/software/workload described enough to reproduce? yes/no
[Baselines] strongest alternative, well-configured, at a matched operating point? yes/no
[Depth] end-to-end AND microbenchmarks present? tails + variance reported?
[Honesty] costs reported beside gains? neutral/negative regions stated?
[Provenance] hardware/software/workload/method/code pinned at collection time? yes/no