known as the utilization factor or traffic intensity factor. The dimensionless load is measured in units called "Erlangs" (Saadawi et al. 1994). The uncertainty in arrival and uncertainty in servicing corresponds to a birth-death stochastic process. Queuing theory refers to this type of arrangement as an M/M/1/00 queue, where M Networks in Telecommunications Calls (network is serving calls) Waiting Service time in capacity queue Figure 2.8. The output of the network is to serve the a calls. The input of the network is the service capacity u. described is standard in telecommunications network planning (Robertazzi 1999). The process is represented in Figure 2.8. If calls exceed system capacity, then a queue forms. A useful measure of service quality is the average time a call spends in the queue before being served. The average waiting time spent in the queue, W, is This is calculated using Little's formula, which specifies the total of average waiting time plus service time, 1/(u-i). To obtain the average waiting time subtract the service time, W = 11/(u-2) - 1/u. The smaller the waiting time in the queue, W, the greater is the service quality. The formula can be rewritten in a form that lends itself to economic interpretation: The call arrival rate 2 is the output of the network, because it is the number of calls per second that are handled by the system. The network serves a rate of calls a with service quality W, which is the average time for a call to begin. The input of the network is the service capacity u, which results from capital investment in the system's nodes and links. Therefore, equation (8) is the production function of the network, A=F(u,W). The network produces a service a of quality W using capacity . Service quality W is a parameter in the production function. Increasing capacity increases output for a given service quality. Increasing service quality by reducing W decreases output for any given capacity . This approach can be generalized by allowing the network to have m servers with possibly