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The key formulas are: Average Queue Length $L = \frac{\lambda}{\mu - \lambda}$, Average Waiting Time $W = \frac{1}{\mu - \lambda}$, and Utilization $\rho = \frac{\lambda}{\mu}$.

Conditions: The model is $M/M/1$.; $\lambda$ is the arrival rate.; $\mu$ is the service rate.; $\lambda < \mu$ for stability.

The key formulas are: Average Queue Length $L = \frac{\lambda}{\mu - \lambda}$, Average Waiting Time $W = \frac{1}{\mu - \lambda}$, and Utilization $\rho = \frac{\lambda}{\mu}$.

Conditions: The model is $M/M/1$.; $\lambda$ is the arrival rate.; $\mu$ is the service rate.; $\lambda < \mu$ for stability.

The first M stands for Markovian (Poisson) arrivals, the second M stands for Markovian (Exponential) service times, and the 1 stands for a single server.

Conditions: The notation refers to a queueing model.; The model is the $M/M/1$ queue.

The first M stands for Markovian (Poisson) arrivals, the second M stands for Markovian (Exponential) service times, and the 1 stands for a single server.

Conditions: The notation refers to a queueing model.; The model is the $M/M/1$ queue.

For an $M/M/1$ queue to be stable, the utilization factor $\rho$ must be strictly less than 1. This means the arrival rate $\lambda$ must be less than the service rate $\mu$.

Conditions: The model is $M/M/1$.; $\rho = \lambda / \mu$.

For an $M/M/1$ queue to be stable, the utilization factor $\rho$ must be strictly less than 1. This means the arrival rate $\lambda$ must be less than the service rate $\mu$.

Conditions: The model is $M/M/1$.; $\rho = \lambda / \mu$.