Applied Statistics and Probability for Engineers

4-10 ERLANG AND GAMMA DISTRIBUTIONS 129

occur in a Poisson process. The random variable that equals the interval length until rcounts

occur in a Poisson process has an Erlang random variable.

EXAMPLE 4-23 The failures of the central processor units of large computer systems are often modeled as a

Poisson process. Typically, failures are not caused by components wearing out, but by more

random failures of the large number of semiconductor circuits in the units. Assume that the

units that fail are immediately repaired, and assume that the mean number of failures per hour

is 0.0001. Let Xdenote the time until four failures occur in a system. Determine the probabil-

ity that Xexceeds 40,000 hours.

Let the random variable Ndenote the number of failures in 40,000 hours of operation.

The time until four failures occur exceeds 40,000 hours if and only if the number of failures

in 40,000 hours is three or less. Therefore,

The assumption that the failures follow a Poisson process implies that Nhas a Poisson distri-

bution with

Therefore,

The cumulative distribution function of a general Erlang random variable Xcan be obtained

from and can be determined as in the previous exam-

ple. Then, the probability density function of Xcan be obtained by differentiating the cumula-

tive distribution function and using a great deal of algebraic simplification. The details are left

as an exercise. In general, we can obtain the following result.

P 1 Xx 2  1 P 1 Xx 2 , P 1 Xx 2

P 1 X40,000 2 P 1 N 32 a

3

k 0

e^44 k

k!

0.433

E 1 N 2 40,000 1 0.0001 2 4 failures per 40,000 hours

P 1 X40,000 2 P 1 N 32

The random variable Xthat equals the interval length until rcounts occur in a

Poisson process with mean has an Erlang random variablewith parameters

and r. The probability density function of Xis

f 1 x 2  (4-17)

rxr^1 ex

1 r 12!

, for x0 and r1, 2, p



 0

Definition

Sketches of the Erlang probability density function for several values of rand are

shown in Fig. 4-25. Clearly, an Erlang random variable with is an exponential

random variable. Probabilities involving Erlang random variables are often determined by

computing a summation of Poisson random variables as in Example 4-23. The probability

density function of an Erlang random variable can be used to determine probabilities;

however, integrating by parts is often necessary. As was the case for the exponential

distribution, one must be careful to define the random variable and the parameter in

consistent units.

r 1



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