In queueing theory, the Engset formula is used to determine the blocking probability of an M/M/c/c/N queue (in Kendall's notation).

The formula is named after its developer, T. O. Engset.

Example application

Consider a fleet of ${\displaystyle c}$ vehicles and ${\displaystyle N}$ operators. Operators enter the system randomly to request the use of a vehicle. If no vehicles are available, a requesting operator is "blocked" (i.e., the operator leaves without a vehicle). The owner of the fleet would like to pick ${\displaystyle c}$ small so as to minimize costs, but large enough to ensure that the blocking probability is tolerable.

Formula

Let

• ${\displaystyle c>0}$ be the (integer) number of servers.
• ${\displaystyle N>c}$ be the (integer) number of sources of traffic;
• ${\displaystyle \lambda >0}$ be the idle source arrival rate (i.e., the rate at which a free source initiates requests);
• ${\displaystyle h>0}$ be the average holding time (i.e., the average time it takes for a server to handle a request);

Then, the probability of blocking is given by^[1]

${\displaystyle P={\frac {{\binom {N-1}{c}}\left(\lambda h\right)^{c}}{\sum _{i=0}^{c}{\binom {N-1}{i}}\left(\lambda h\right)^{i}}}.}$

By rearranging terms, one can rewrite the above formula as^[2]

${\displaystyle P={\frac {1}{{}_{2}F_{1}(1,-c;N-c;-1/(\lambda h))}}}$

where ${\displaystyle {}_{2}F_{1}}$ is the Gaussian Hypergeometric function.

from scipy.special import hyp2f1
P = 1.0 / hyp2f1(1, -c, N - c, -1.0 / (Lambda * h))

P = 1 / hypergeom([1, -c], N - c, -1 / (Lambda * h))

Unknown source arrival rate

In practice, it is often the case that the source arrival rate ${\displaystyle \lambda }$ is unknown (or hard to estimate) while ${\displaystyle \alpha >0}$, the offered traffic per-source, is known. In this case, one can substitute the relationship

${\displaystyle \lambda h={\frac {\alpha }{1-\alpha (1-P)}}}$

between the source arrival rate and blocking probability into the Engset formula to arrive at the fixed point equation

${\displaystyle P=f(P)}$

where

${\displaystyle f(P)={\frac {1}{{}_{2}F_{1}(1,-c;N-c;1-P-1/\alpha )}}.}$

References