Common network benchmarks

1) Why do we need "general benchmarks"

• compare domains/nodes/providers by single SLO;
• Manage network settings (rates, quotas, limits) based on facts
• identify regressions before incidents in the product;
• make incentives (bonuses/penalties) and trust transparent.

2) Taxonomy of metrics

2. 1 Performance

Latency: p50/p95/p99, tails, cold-start.
Throughput: msgs/s, tx/s, GB/s (DA/storage), RPS (API).
Availability: SLO success, share of timeouts/retrays.
Ordering & Exactly-Once: out-of-order %, duplicate ratio.

2. 2 Reliability and stability

SLA breaks/1k events, MTBF/MTTR, QoS degradation.
Backpressure-efficiency: stabilization time after burst.

2. 3 Safety

Integrity/order theft incidents (bridge, x-domain).
Authentication/authorization quality: percentage of rejected/false tolerances.
Anti-fraud signals: TPR/FPR behavioral patterns.

2. 4 Economics

Cost-to-Serve/request, margin/message, revenue/DA byte.
Resource efficiency: CPU/GPU-util, IOPS/GB, egress/request.
Fairness: "noisy neighbor" index, quota allocation.

2. 5治理 and Processes

Parameter-convergence speed, success of recoilless releases,

processing time of proposals, share of votes with R-modifier.

3) Traffic profiles and QoS classes

Q4 (critical commands): small messages, strict deadlines.
Q3 (ordered flows): key-partitioning, order guarantee.
Q2 (exactly-once effective): idempotency + deadup.
Q1 (at-least-once): telemetry, mass events.
For each class, we set reference profiles: message size, frequencies, proportion of synchronous/asynchronous calls, bursts, correlations.

4) Bench Suite

1. Messaging Core: 1→N и N→1; growth of RPS to saturation; measurement of p95 and duplicate ratio.
2. Low-Latency API: read/write mix, cold/warm cache, limits and degradation.
3. DA/Storage: Publication batches, Throughput/GB metering and finalities.
4. X-Domain/Bridge: proofs, finality, challenge periods, losses/redeliveries.
5. ML-Inference Edge: POP latency/skip, overload degradation.
6. Batch & Stream: ETL windows, consumer lags, backpressure efficiency.
7. Security & Abuse: synthetic fraud patterns, anti-fraud load, FPR/TPR.
8. Failover/Chaos: AZ/pool off, stopcocks, SLO return time.

5) Measurement methodology

5. 1 Replicability

Fixed versions of schemas/SDK/configs; "seeded" load generators.
Warm-up ≥ N minutes; measurements in the stable phase ≥ M minutes.
Trace/span and log correlation.

5. 2 Honesty and anti-gaming

Split setup phase and blind-run (hidden load profile).
Hidden control tasks (checking cache "wrappers "/special optimizations for signatures).
Set of black tests: unexpected fields, microsplices, "rare" sizes.

5. 3 Formulas

SuccessRate = 1 − (timeouts + errors)/requests

TailAmplification = p99/p50, Headroom = (cap − current)/cap

Cost/Req = Σ (resource bid )/successful _ requests

FairnessIndex (Jain) for quotas/bands.

6) SLO and reference targets (benchmarks)

Q4 API: p95 ≤ 200 ms, success ≥ 99. 99%, errors ≤ 1/10⁴.
Messaging Q3: violation of the order of ≤ 10⁻⁶/soobshch., p95 ≤ 500 ms.
DA publications: finality ≤ 3 × T _ block, Throughput ≥ X GB/h.
Bridge: false confirmations = 0; MTTR abnormalities ≤ 1 h.
Stream: lag ≤ 2×window; drop = 0 for critical topics.
Batch: Window jabs fit into the T_window with a margin ≥ 20%.

7) Artifacts and report format

Passport of the run: versions, configs, date/time, geo.
Graphs: latency (pXX), throughput, lags, resource utilization.
SLO mapping tables: pass/fail + delta to reference.
Capital regressions: list with RCA and fix plan.
Economy: Cost-to-Serve, margin/message, hotspot-nodes.
Conclusion: "Ready for release/Tuning needed/Blocker" status.

8) Relationship with tariffs and limits

If TailAmplification grows → automatically lower quotas or increase the price of "noisy" tenants.
Nodes with SLA breaks lose their share of rewards (slashing) before recovery.
Domains with stable quality receive a reduced take-rate (quality bonus).

9) Observability of benchmarks

End-to-end tracing of all benchmark requests.
DLQ/Replay for failed events and idempotence confirmation.
Дашборды: BenchRun Live, Tail Heatmap, Backpressure Monitor, Bridge Risk, DA Throughput.

10) i治理 processes

Pre-release gate: can only be released when'SLO _ pass> = target threshold'and there are no security locks.
Change Impact: Each significant configuration/version passes a short smoke-bench.
Sunset-SLO: temporarily increased requirements for pilots; auto-rollback by date.
R-modifier of votes: in disputes about the metric, participants with a high R-reputation for quality have more weight.

11) Benchmark launch playbook

1. Collection of requirements: critical path circuits, QoS classes, business SLOs.
2. Profile design: message sizes, R/W mix, bursts, x-domain share.
3. Load tools: generators, data fixes, synthetic fraud patterns.
4. Observability: tracing, metrics, policy logs, error budget.
5. Benchmark targets: SLOs, economic thresholds, fairness corridors.
6. Pilot run: calibration, bottleneck detection, fix.
7. Regularization: nightly/weekly benchi + reporting in kaznacheystvo/治理.
8. Incidents: chaos supplements, post mortems, test updates.

12) Anti-gaming and measurement ethics

Prohibition of "special optimizations for the bench signature" without improving real production traffic.
Blind loads, random "noise" parameters, control events.
Public reports with methodology; arbitration committee for controversial cases.

13) Typical "red flags"

p95 is stable, but p99. 9 sharply growing → hidden competition for resources.
Throughput is high, but duplicate ratio ↑ → incorrect idempotency.
Good latency, but Cost/Req does not converge → cross-dependency/double entry.
Low lag, but DLQ depth is growing → errors in retras/quarantine.

14) Benchmarking Program KPI

Coverage: the proportion of critical paths with regular benches ≥ X%.
On-time report ≤ Y hours after the run.
Quality: number of regressions caught before the pre-incident; mean delta to SLO after fix.
Economy: Cost-to-Serve decline/inquiry and "noisy neighbour" numbers.
治理: rate of reactions on bench regression; transparency of public reports.

15) Delivery checklist

• Fixed load profiles and QoS classes
• Configured Trace, Metrics, DLQ/Replay
• SLOs/thresholds and fairness corridors defined
• Anti-gaming protection and blind tests enabled
• Report format and release gate process described
• Regular (nightly/weekly) runs
• Integrated chaos/failover unit
• Public post-mortems and performance test improvement

16) Glossary

Bench Suite: a set of reference scenarios and load profiles.
TailAmplification: p99/p50 ratio (tail strength).
FairnessIndex (Jain) -Resource uniformity metric.
DLQ/Replay: quarantine and reprocessing events.
SLO/SLA: target service levels/contractual guarantees.
Blind-run: a hidden run against anti-gaming.

Bottom line: common benchmarks turn network performance and stability into manageable parameters, linking technology and i治理 economics. Standardized scenarios, transparent reports and anti-gaming policies ensure comparability of results, member trust and ecosystem evolution without guesswork and "magic."