Estimating microservice operation availability requires connecting runtime observations to completion of the required work within a deadline. Detailed architectural models provide one route, but their reconstruction and analysis can be costly. We present algorithms for identifying and analysing a reduced stochastic availability model from telemetry and declared operation requirements. The identification procedure constructs a joint law of execution states and retains compatible laws when observations are incomplete. Formal results establish attainable probability bounds and conditions for identifying a completion-and-deadline representation directly. This construction preserves the range obtained by evaluating the same completion-and-deadline event in the full model; a separate joint comparison describes the structural information discarded. An independent study covered three microservice applications and 18 campaigns. On complete common campaigns with point forecasts from both methods, observed mean absolute errors differed from those of independently extracted PMX/Palladio architectural models by at most 0.082 percentage points. The full graph-based workflow from telemetry to first forecast was 31.73-100.24 times faster than the studied PMX/Palladio workflow, including startup and launcher waits. Direct identification preserved the retained forecasts and intervals, with median paired first-answer savings of 5.1-11.2% relative to full execution-model analysis. The results connect required model detail to measured identification and analysis costs.

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