Skills Engineering Guidelines for Artifact Evaluation in Cloud Systems

Guidelines for Artifact Evaluation in Cloud Systems

v20260724
socc-artifact-evaluation
This guide outlines the best practices for preparing system artifacts for academic evaluation, following the ACM Artifact Review and Badging scheme. It details how authors must ensure reproducibility, especially for tail latency and cost results, by providing scaled-down testbeds, comprehensive documentation, and clear provenance tracking to satisfy conference evaluators.
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Overview

SoCC Artifact Evaluation

Use this for artifact preparation. SoCC — as an ACM venue — follows the ACM Artifact Review and Badging scheme where an edition offers evaluation. First, verify whether the current SoCC edition runs a dedicated artifact-evaluation track, and which badges it offers — unlike some sibling systems flagships that run a standing AE process, SoCC's artifact track and badge set are decided per edition and are 待核实 as of 2026-07-09. The advice below applies once the edition's call confirms evaluation.

Two things to internalize: badges are earned by evaluators actually reproducing your cloud results, and the review artifact (anonymized, for the paper's reviewers) is not the same deliverable as the badge artifact (de-anonymized, permanently archived).

The ACM badges (verify the current set and names)

Badge What it certifies What earns it for a cloud paper
Artifacts Available The artifact is permanently, publicly retrievable Deposit in a DOI-issuing archive (Zenodo, figshare, Software Heritage)
Artifacts Evaluated - Functional The artifact runs and does what the paper says A documented testbed setup, a workload replay, and expected outputs
Artifacts Evaluated - Reusable Others can build on it Functional plus careful docs, structure, licensing, and a portable harness
Results Reproduced An evaluator reproduced the paper's key results A turnkey path from the artifact to the headline throughput/tail/cost numbers

Available is a low-cost, high-value badge (archive the package); Functional/Reusable/Reproduced require the evaluator's own run to succeed, so the failure mode is always "did not run on their testbed," never "the idea was weak."

What a cloud-systems evaluator opens first

Claim type First thing inspected Common failure caught
A cloud system/mechanism The README and one setup+run command Undocumented cluster assumptions; only-runs-on-authors'-testbed
A measurement/trace study The scripts that turn the trace into the paper's figures Numbers in the PDF no script reproduces; trace missing
A scheduling/serverless result The workload generator + the tail/cost measurement scripts Only the mean reproduces; tail and cost cannot be regenerated
A large-scale deployment A scaled-down but faithful reproduction path Requires a proprietary cluster; no smaller-scale replay

Assume an evaluator has a bounded time budget and cannot reserve your 200-node cluster. Provide a scaled-down reproduction that still regenerates the shape of the tail and cost results, plus a clear statement of what needs full scale.

Packaging plan

[Environment] ship a Dockerfile / pinned environment AND a testbed description (node counts,
              instance types, OS, kernel) so a run is reproducible
[Workloads]   the workload generator or the (anonymized, then released) trace, not just a pointer
[README]      one-screen orientation: what it is, how to set up, how to run a small demo, how to
              reproduce each figure, expected runtime and outputs
[Mapping]     an explicit table: paper claim -> script -> expected result (incl. tail and cost)
[Scaled path] a small-scale reproduction that runs without the full cluster
[Provenance]  commit SHAs, trace extraction dates, instance types, seeds, run counts
[License]     an OSI-approved license so the artifact can be badged Reusable
[Archive]     deposit in a DOI-issuing repository for the Available badge

Anonymized review artifact vs. badge artifact

  • At submission: the artifact is anonymized for the paper's reviewers — no cluster names, provider hints, owner strings, or identity-revealing trace provenance, and no live repository that discloses authors.
  • After acceptance: replace anonymized placeholders with the public, licensed, DOI-issuing archive; this is the version any artifact evaluators badge and the camera-ready cites.

Worked vignette: packaging an autoscaler + trace study

A paper contributes a tail-aware autoscaler and a measurement of the cost-tail gap. To target Reusable and Reproduced: ship a Docker image with the controller pre-built; a run_demo.sh that replays a short trace slice on a few nodes in minutes and prints p99 and instance-seconds; a reproduce/ directory whose scripts regenerate each figure from logged runs; a claim-to-script mapping table in the README; the (released) trace-replay harness with pinned SHAs; and an MIT/Apache license. State honestly which figures are turnkey at small scale and which need the full testbed.

Calibration

  • Confirm the track exists for this edition before planning; SoCC's AE track and badge set are 待核实 per cycle.
  • Reproducing tail and cost, not just the mean, is the cloud-specific bar; design the package so an evaluator can regenerate them.
  • Badge names, the exact set offered, and whether evaluation is single- or double-anonymous vary by edition — confirm on the current call.

Output format

[Track status] SoCC AE track confirmed for this edition? yes/no/待核实
[Target badges] Available / Functional / Reusable / Reproduced
[Artifact role] anonymized review artifact / public badge artifact
[Contents] <system/workloads/trace/scripts/testbed-desc/provenance/license>
[Small-scale test] does setup + demo reproduce tail+cost shape without the full cluster? yes/no
[Claim mapping] <claim -> script -> expected result (incl. tail/cost) present? yes/no>
[Fixes before upload] <ordered list>
Info
Category Engineering
Name socc-artifact-evaluation
Version v20260724
Size 5.93KB
Updated At 2026-07-29
Language