# Eurosys Experiments > Use when designing or auditing the evaluation of a EuroSys paper — choosing workloads that earn the word realistic, tuning baselines beyond their defaults, decomposing end-to-end wins into per-mechanism gains, measuring overheads and worst cases, and sizing experiments to the claims the paper actually makes. - Skill: `thedixitjain/eurosys-experiments` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/eurosys-experiments` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/eurosys-experiments/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/eurosys-experiments --- # EuroSys Experiments Use this while the evaluation is still designable. EuroSys reviewers treat the evaluation as the paper's testimony about itself: the design section says what should happen, and §Evaluation is cross-examination. Plan it as a set of questions with falsifiable answers, not as a benchmark tour. ## The four-layer evidence stack | Layer | Question it answers | Typical EuroSys instrument | |---|---|---| | End-to-end | Does the system win where users live? | Full application or serving workload, realistic scale | | Decomposition | Which mechanism buys which fraction? | Factor analysis: enable components one at a time | | Cost | What does the win spend? | Memory/CPU/network overhead, code and ops complexity | | Boundary | Where does it stop winning? | Adversarial mixes, saturation, failure injection | A submission with only the first layer reads as a demo; the middle layers are what convert "it is faster" into "we understand why it is faster", which is the systems-research standard of proof. ## Workload realism, argued not asserted - Prefer published traces and standard suites with named versions; when using a synthetic generator, calibrate it against a real distribution and say how. - Match the workload to the claim's regime: a memory-efficiency claim needs memory pressure; a tail-latency claim needs load near saturation, not at 30%. - Scale honestly: if the pitch is rack-scale, a two-node result needs an explicit extrapolation argument or a scoped-down claim. - Report the workload's own parameters (skew, read ratio, arrival process) so the experiment is reconstructible without your cluster. ## Baseline fairness — the venue's sharpest knife The reflexive EuroSys reviewer question is "did they tune the baseline?" Answer it before it is asked: - Run baselines at their documented best configuration for your hardware, and cite where that configuration comes from. - Include the strongest *deployed* alternative, not only research prototypes — losing to a well-tuned production system on some axis is survivable; omitting it is not. - Same hardware, same workload generator, same measurement harness for every system; any asymmetry gets a sentence of justification. - When your system loses a metric, plot it anyway and explain the tradeoff. ## Experiment matrix as an artifact Freeze the plan in a machine-checkable form before running: ```yaml # eval-matrix.yaml — one row per claim the paper will make - claim: "cuts p99 GET latency ≥40% under skewed load" figure: fig8 workload: {trace: twitter-cache-2020, skew: zipf-0.99, load: 0.85sat} systems: [ours@v1.4, baselineA@3.2-tuned, baselineB@1.9-tuned] reps: 10 metrics: [p50, p99, p999, throughput] status: pending ``` The matrix doubles as the reproducibility ledger (`eurosys-reproducibility`) and later as the artifact's claims map (`eurosys-artifact-evaluation`). ## Result invalidators to design out early Each of these has sunk otherwise strong EuroSys evaluations; each is cheap to prevent and expensive to discover in a review: - Measuring through a bottleneck that is not the system under test — a saturated client, a slow log disk, a debug build of a dependency. - Comparing your warm system against cold baselines (or vice versa) because warm-up policy was never standardized across systems. - A "scalability" curve whose x-axis grows load and resources together, so nothing about scaling behavior is actually isolated. - Reporting means over runs that include documented failures — decide the failure-handling policy for measurements before running them. - Config drift between the end-to-end and decomposition experiments, so the component gains do not sum to anything resembling the headline. ## Sizing runs for the claim - Comparative bar charts: ≥5–10 repetitions with dispersion shown; single-run bars invite a one-line rejection rationale. - Tail latencies: enough requests that the quoted percentile has support — a p999 from 10k requests is ten samples of noise. - Ablations: vary one factor per experiment; a config delta of two changes attributes nothing. - Timeboxing: reserve cluster time for the boundary layer explicitly; it is always the layer teams drop under deadline pressure and the layer reviewers miss loudest. ## Reporting floor for the paper - Hardware and topology for every experiment, once, in a table the reader can find (§Evaluation setup), with per-figure deviations noted. - Software versions for every system in every comparison, including yours. - Load points quoted with the metric ("85% of measured peak throughput"), never as bare client counts whose meaning depends on the cluster. - Repetition count and dispersion type in every caption that shows a comparison. - A sentence on measurement methodology for anything subtle: how latency is timestamped, where the measurement harness sits, what it costs. ## Output format ```text [Claim -> evidence map] [Stack coverage] end-to-end / decomposition / cost / boundary: present or missing [Baseline fairness] [Realism audit] [Highest-value missing run] ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `EuroSys-Skills/skills/eurosys-experiments/SKILL.md`