Measure rollout visibility across resolvers and regions. Compare observed adoption against expected cache expiry trends. Reduce risk with smarter timing before critical DNS migrations.
1) Observed Adoption
(Locations Seeing New Value / Total Locations Checked) × 100
2) Theoretical Cache Release
100 × [1 - e^(-Cache Decay Factor × Time Since Change ÷ Old TTL Minutes)]
3) Weighted Propagation
(0.55 × Observed Adoption) + (0.25 × Theoretical Cache Release) + (0.20 × Authoritative Sync)
4) Confidence Score
This score combines sample size, probe rounds, and authoritative alignment. More locations and repeated checks improve confidence.
5) Estimated Remaining Time
The model solves the decay curve to reach your target completion level, then adjusts remaining time when observed adoption trails the cache model.
Important TTL note
Lowering the TTL after a change helps future cache entries, but already cached records generally remain governed by the old TTL.
| Minute Mark | Theoretical Cache Release (%) | Example Observed Adoption (%) | Locations on New Value | Locations Still Stale |
|---|---|---|---|---|
| 0 | 0.0 | 7.7 | 2 | 22 |
| 15 | 25.0 | 29.7 | 7 | 17 |
| 30 | 43.7 | 46.2 | 11 | 13 |
| 60 | 68.3 | 67.8 | 16 | 8 |
| 120 | 90.0 | 86.9 | 21 | 3 |
| 180 | 96.8 | 92.9 | 22 | 2 |
This example table is illustrative. Your live inputs may produce different adoption patterns.
It estimates DNS propagation progress from probe coverage, resolver adoption, TTL timing, and authoritative alignment. It is a planning and monitoring model rather than a live DNS testing network.
Resolvers that cached the earlier answer normally keep it until that older TTL expires. Lowering the TTL after the change does not instantly shorten those already existing cache entries.
It controls how quickly the theoretical model releases stale caches. Larger values make the curve rise faster. Use conservative values when resolver behavior is uneven or unpredictable.
Weighted propagation blends observed resolver results, modeled cache expiry, and authoritative sync. It helps summarize progress into one operational metric for release planning.
Resolvers may refresh at different times, some probes may share recursive infrastructure, and geographic samples can be uneven. Authoritative mismatches can also delay full adoption.
Not perfectly. Some caches, forwarders, client systems, or corporate resolvers may trail behind. Most teams use a practical threshold such as 95 percent or 99 percent.
Trust it more when you check many locations across multiple rounds and your authoritative servers are fully aligned. Small samples naturally reduce certainty.
No. It complements them. External checkers provide live observations, while this calculator helps quantify progress, explain delays, and estimate the remaining propagation window.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.