Changed-Data Rate and Effective Bandwidth as Predictors of Cloud Migration Cutover Readiness: A Controlled Benchmark
Abstract
Migration teams often estimate cutover readiness using dataset size alone, although changed-data rate and effective transfer bandwidth determine whether replication can converge before the planned outage window. We performed a controlled benchmark across 216 migration runs involving datasets from 2 to 40 TB, six change-rate profiles, and four network conditions. Pre-cutover telemetry was used to model final synchronisation duration and the probability of meeting a predefined cutover window. Changed-data rate and effective bandwidth jointly explained 82% of variation in final synchronisation time, compared with 39% for dataset size alone. A readiness model based on the ratio of changed-data generation to sustained transfer capacity correctly classified 91% of successful cutovers and 87% of overruns. Prediction errors increased when compression ratio or network contention changed abruptly within two hours of cutover. Incorporating a short stability window improved calibration without materially delaying migration decisions. The findings support the use of dynamic convergence indicators rather than static capacity measures when determining cloud migration readiness, particularly for write-intensive systems where replication lag can change rapidly before cutover.
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