Have a look at Queuing Theory. Choose a model fits your system ( here is a good list , note there are other models apart from the ones listed in the book).
For example, let's use a M/G/k queue, average delay/waiting time can be calculated: $Eg= \frac{C^2 +1}{2}Em$ C is the coefficient of variation of the service time distribution, in your case it would be zero, since you service time is always 100 ms. $Em =\frac{ C(c,\frac{\lambda}{\mu}) }{c\mu-\lambda} + \frac{1}{\mu}$ $C(c,\frac{\lambda}{\mu})$ is the probability that an arriving customer is forced to join the queue (all servers are occupied), referred to as Erlang's C formula. $ C(c,\frac{\lambda}{\mu}) = \frac {1}{ 1+(1-\rho)\frac {c!}{ (c\rho )^{c} } \sum _{k=0}^{c-1}{\frac { (c\rho )^{k} }{ k! }} } $ where $\rho$ is server utilization, $c$ is the number of servers. $\rho = \frac{\lambda}{ c*\mu }$
Let $duration$ be the total time of your observation/experiment, which is the sum of requests inter-arrival times. Request arrival rate is $\lambda = \frac{\text{count of requests}}{duration}$ Let the average time it takes a server to service a request be $Ts$, then the average servicing rate would be (requests/second) $\mu=\frac{1}{Ts}$ In this case, I generated 100 random numbers, with value between 5 and 15, to simulate your requests inter-arrivals.
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$duration = 990$ seconds
$count=100$ requests
$\lambda = 0.10101$ requests/s
$\mu = 10$ requests/s
$\rho = 0.010101$
$C(c,\frac{\lambda}{\mu}) = 0.010101$
$Em=0.10102$ seconds
$Eg=0.0505102$ seconds
The number of users (your $n$) has no direct relation with the model, the parameter changes the behavior of the Queue would be the request arrival distribution, which maybe affected by the number of users.