# Fast Experiments > Use when designing or auditing a USENIX FAST storage evaluation, covering real devices and firmware, device-state control (aging, preconditioning, fill, TRIM), standard workloads and traces (SNIA IOTTA, YCSB, filebench, fio), write amplification, tail latency, endurance and wear, crash-consistency testing, fair baselines, and matching the metric to the shape of each storage claim. - Skill: `thedixitjain/fast-experiments` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/fast-experiments` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/fast-experiments/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/thedixitjain/fast-experiments --- # FAST Experiments Use this before submission when the storage evaluation is not yet locked. FAST reviewers are storage people; the evaluation is where a good idea is won or lost, and the questions are storage-specific. The organizing principle is **measure the storage cost you claim to change, on real hardware in a realistic state** — not a throughput bar on a fresh drive. ## Evaluation audit - **Match the metric to the storage claim.** A claim about *endurance* needs bytes-written / P/E cycles, not throughput; a claim about *responsiveness* needs **tail latency** (p99/p99.9), not the mean; a claim about *space* needs measured on-media footprint; a claim about *durability* needs a crash-consistency test. The wrong metric is the most common FAST reject. - **Use real devices, and name them.** Model, capacity, interface (SATA/SAS/NVMe), and — critically — **firmware version**, plus host, kernel, and filesystem/mkfs options. Nominally identical drives differ part-to-part and across firmware; a result without the device table is not auditable. - **Control device state.** SSDs must be **preconditioned to steady state** (fresh-out-of-box numbers flatter every design); report fill level and TRIM/discard state. For file systems, report whether the volume was **aged/fragmented** or empty — aging can dominate the result. - **Drive with credible workloads and traces.** Standard generators (fio, filebench, YCSB for KV/DB) and archived traces (SNIA IOTTA block/object traces, production-derived traces) beat an ad-hoc microbenchmark. Ship the job files and replay scripts. - **Choose fair baselines,** including the strongest prior system and a reasonable default, tuned with a documented, equal budget. An untuned or default-config baseline is a scored weakness. - **Test the invariant your optimization risks.** If a change defers writes, batches, or reorders, show crash consistency still holds (record-and-replay / fault injection), not just that it is faster. - **Design threats in, not on:** know before you run which confounds (device variance, thermal throttling, cache effects, contamination of a trace) will bite, and instrument to bound them. ## Claim-to-evidence design table | Storage claim | Matching evidence | Reject pattern avoided | |---|---|---| | "Cuts write amplification / extends endurance" | Bytes-written from device counters (SMART/logs) at steady state; projected P/E budget | "Estimated WA on a fresh drive; no device counters" | | "Lower/steadier latency" | Full latency distribution incl. p99/p99.9 under load | "Reports mean latency only" | | "Scales to real capacities/workloads" | Real-sized datasets and standard traces on real devices | "Tiny dataset on a simulator" | | "Preserves crash consistency" | Fault-injection / block-level record-and-replay recovery test | "Claims consistency, never crash-tests it" | | "Faster than system X" | X tuned with equal, documented budget; same hardware and state | "Default-config or older-hardware baseline" | | "Reliability finding generalizes" | Population, models, and duration stated; external validity bounded | "One model, one datacenter, claimed universal" | ## Device-state and measurement floor ```text [Devices] list model, capacity, interface, firmware; host, kernel, mkfs/mount options [Steady state] precondition SSDs to steady state; state fill level and TRIM; disclose FOB vs. aged [Warmup] discard cold-cache warmup unless the cold path IS the claim; state cache/DRAM sizes [Repeats] multiple runs; report variance/CIs; note thermal or throttling effects [Counters] read WA, bytes-written, GC activity from device logs where available, not estimates [Trace replay] replay archived traces with a documented tool; state timing fidelity (open vs. closed loop) ``` ## Crash-consistency and durability testing Durability claims are load-bearing at FAST and are frequently under-tested: ```text [Model] state the failure model (power loss, kernel panic, fsync semantics) [Injection] use block-level record-and-replay or a fault injector to cut writes at many points [Check] verify the post-recovery state satisfies the invariant (no torn/lost committed data) [Coverage] report how many crash points / orderings were tested, not a single anecdote ``` ## Provenance floor for traces and field studies - Archive the *replayed* trace (or document access) and the replay scripts; a trace named but not shipped cannot be reproduced. - For field/reliability studies, state the population, drive models, technology (SLC/MLC/TLC/QLC), duration, and how failures/replacements were defined and counted. - Report how outliers, warmup, and defective units were handled — silent inclusion or exclusion skews every downstream number. ## Vignette: evaluating a compaction change for a KV store Suppose the paper claims an endurance-aware compaction scheduler cuts bytes written. The matching plan: run on **named SSDs at steady state** with firmware recorded; drive with **YCSB plus an archived production trace**; measure **bytes-written from the device's own counters**, not the LSM's estimate; report **read-latency distributions incl. p99.9** to prove the trade is bounded; compare against the **tuned** stock compactor with an equal budget; and run a **crash-consistency record-and-replay** test to confirm deferring compactions did not weaken durability — every number traceable to a logged run in the artifact. ## Output format ```text [Evaluation readiness] strong / adequate / weak [Claim -> metric map] [Device reality] [Baseline fairness] tuned? equal budget? same hardware/state?> [Durability check] [Threats-by-design] instrumentation> [Decision-critical next run] ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `FAST-Skills/skills/fast-experiments/SKILL.md`