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7.3 Control-Theoretic View for Policy-Driven Activators View activator thresholds as controllers that sample system state and trigger corrective action. Analyze stability: oscillation risk (thrashing) when activation frequency inadvertently causes state changes that retrigger activations. Provide hysteresis, debounce, rate-limiting to improve Precision and Availability.
IRA = sum_i w_i * v_i
7.2 Reliability Model Use Bernoulli trials for trigger success; model correlated failures with Markov chains to capture outage periods (e.g., activator service down → R drops).
Activator B (webhook): different values; compute IRA_B and compare.
7.3 Control-Theoretic View for Policy-Driven Activators View activator thresholds as controllers that sample system state and trigger corrective action. Analyze stability: oscillation risk (thrashing) when activation frequency inadvertently causes state changes that retrigger activations. Provide hysteresis, debounce, rate-limiting to improve Precision and Availability.
IRA = sum_i w_i * v_i
7.2 Reliability Model Use Bernoulli trials for trigger success; model correlated failures with Markov chains to capture outage periods (e.g., activator service down → R drops). index of reloader activator
Activator B (webhook): different values; compute IRA_B and compare. compute IRA_B and compare.