Thresholds Before Labels: Ceiling Quantization Determines When Autoscaling Signals Change Replica Decisions

Reactive autoscalers compress workload state into a scalar, but bounded ceiling partitions jointly induced by signal and target determine replica recommendations. We formalize five signals and distinguish the positive target, bounded desired recommendation, and deployed workers. We prove exact recommendation equivalence, a tight charge-estimation error bound, and an expected pre-bound quantization premium for i.i.d. mean-matched charges. A 100-seed executable check verified theorem implementation, and 27,900 simulations mapped threshold–capacity trade-offs. Four paired Windows process-level faster-whisper studies tested near-equivalence, partition crossing, and target relocation. E4 provided a local adjacent-frontier comparison in 60 fresh three-arm blocks; count targets 1.00 and 0.90 and a mean-matched work target replayed the same 48-request manifest in each block. Count 0.90 versus 1.00 improved deadline attainment by +2.92 pp (95% CI +0.97 pp to +4.90 pp) while increasing capacity by +0.0322 worker-s/audio-s. Work differed from count 1.00 by +2.57 pp and from count 0.90 by −0.35 pp. An independent audit found 134/137 held-out bundles deadline-feasible in isolated service; conditioning preserved both the E2 work-minus-count effect (+2.56 pp) and the E4 target effect (+2.96 pp). Within the tested partitions, these results separate threshold relocation from signal information and motivate testing adjacent targets before adopting a richer estimator; they do not establish the global count-target frontier.