Socc experiments

SoCC Experiments

Use this before submission when the evaluation is not yet locked. SoCC reviewers come from both SIGMOD and SIGOPS, and the evaluation is where a good cloud idea is won or lost. The organizing principle is measured evidence proportional to the claim — the study must test the thing the paper asserts, on a system and workloads a skeptic from either community would accept, with tail latency and cost reported, not just the mean.

Evaluation audit

• Prefer a real or realistic deployment over simulation. A cloud claim about a system needs the system running on a testbed or cluster; simulation alone invites the "does it hold on real hardware?" reject. Where full scale is impossible, run a faithful scaled deployment and say so.
• Use production or representative workloads/traces, with stated provenance and selection, and release the replay harness. Synthetic-only microbenchmarks invite the "does this reflect real load?" objection.
• Report tail and cost, not just averages. p95/p99 (p99.9 where it bites), the latency distribution, and a concrete cost model (instance-seconds or $) are the outcomes operators are billed against.
• Choose fair baselines, including the strongest prior system and a simple-but-reasonable alternative, each tuned with a documented, equal budget. An untuned baseline is a scored weakness.
• Show scale and multi-tenancy behavior: how the result holds as nodes, tenants, or load grow, and where the bottleneck appears — SoCC papers live at scale.
• Make the measurement pipeline reproducible (see the code block) so the evaluation reproduces rather than re-samples.
• Design limits in, not on: know before you run which provider, region, and regime limits the study will have, and instrument to bound them.

Claim-to-evidence design table

Cloud claim	Matching evidence	Reject pattern avoided
"System raises throughput at scale"	Throughput across node counts on a real testbed vs. tuned baseline	"Simulated / small-scale only"
"Holds tail latency under bursts"	p99 distribution under bursty replayed load	"Only mean latency reported"
"Cuts cost"	Instance-seconds or $ under a stated pricing model	"Cost asserted, never measured"
"Fair under multi-tenancy"	Per-tenant SLO attainment with contending workloads	"Single-tenant microbenchmark"
"The new mechanism adds the value"	Ablation isolating the mechanism vs. a simpler policy	"Mechanism's marginal value never isolated"

Tail, cost, and scale floor

[Tail]   report percentiles and the distribution, not just the mean; state the SLO target
[Cost]   define the pricing model; report the cost metric so a reader can recompute the saving
[Scale]  sweep nodes/tenants/load; identify the bottleneck where the result degrades
[Runs]   report the number of runs and the variance for any measured metric
[Compute] state the testbed actually used (nodes, instance types, kernel), not vague feasibility

Workload and trace provenance floor

• Pin commit SHAs and record trace extraction dates; archive the replayed trace or a faithful generator, not just a pointer or a query.
• State inclusion/exclusion criteria and the resulting workload, with the filtering/replay scripts in the artifact.
• Report how the trace maps onto the testbed (scaling factors, time compression) so a reader can judge realism.

Vignette: evaluating a storage QoS mechanism

Suppose the paper claims a scheduler enforces latency SLOs on shared storage better than the prior system. The matching plan: run both on a real storage testbed with a mix of latency-sensitive and batch tenants replayed from a representative workload; tune both with an equal, documented budget; report per-tenant p99 attainment and aggregate throughput with variance; show behavior as tenants and load scale and name the bottleneck; report the provisioning cost of each; and state external-validity limits (storage backend, workload mix) — every number traceable to a logged run in the artifact.

Statistical and measurement reporting floor

• Latency distributions and tail percentiles for every comparison, not point estimates.
• Number of runs and the source of variance for any measured metric.
• A stated cost model behind any economic claim; the compute actually consumed.

Output format

[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: testbed/workload/metric incl. tail+cost>
[Deployment realism] <real testbed / scaled deployment / simulation-only>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Tail+cost+scale] <percentiles + cost model + scaling behavior reported? yes/no>
[Provenance] <trace SHAs+dates, replay harness, testbed described? yes/no>
[Decision-critical next run] <one experiment or measurement extension>