Strategic Technical Decision Making Questions

import random
import statistics

def simulate_one(N, H, P):
    # Initially all N instances healthy; 0 upgraded
    healthy = N
    upgraded = 0
    steps = 0
    slo_violated = False

    # Upgrade instances one by one until all upgraded
    while upgraded < N:
        steps += 1
        # Simulate upgrade of one instance: it either becomes healthy (pass) or unhealthy (fail)
        if random.random() < P:
            # upgraded instance becomes healthy (still counts as healthy)
            # healthy remains the same because we replaced a healthy with a healthy
            pass
        else:
            # upgraded instance is unhealthy => healthy count decreases by 1
            healthy -= 1

        upgraded += 1

        # Check current healthy fraction
        if healthy / N < H:
            slo_violated = True
            # note: we still continue to finish upgrades to count steps, but violation recorded

    return steps, slo_violated

def monte_carlo(N, H, P, trials=20000, seed=0):
    random.seed(seed)
    steps_list = []
    slo_ok_count = 0
    for _ in range(trials):
        steps, violated = simulate_one(N, H, P)
        steps_list.append(steps)
        if not violated:
            slo_ok_count += 1
    expected_steps = statistics.mean(steps_list)
    prob_slo = slo_ok_count / trials
    return expected_steps, prob_slo

# Example
if __name__ == "__main__":
    N = 10
    H = 0.8
    P = 0.95
    exp_steps, prob_slo = monte_carlo(N, H, P, trials=50000)
    print(f"Expected steps: {exp_steps:.2f}, Probability SLO maintained: {prob_slo:.4f}")