Question

Show that $$ W[t]=1-\sum_{n=0}^{K-1} q_n\left(e^{-\mu t} \sum_{k=0}^n \frac{(\mu t)^k}{k !}\right) $$ for the $\mathrm{M} / \mathrm{M} / 1 / \mathrm{K}$ queueing system, where $$ q_n=\frac{p_n}{1-p_K} $$ [Hint: Write $$ \begin{aligned} W[t] & =\sum_{n=0}^{K-1}\left\{\int_0^t \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x\right\} q_n \\ & =\sum_{n=0}^{K-1}\left\{1-\int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x\right\} q_n \\ & =1-\sum_{n=0}^{K-1} q_n \int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x . \end{aligned} $$ Then make the change of variable $y=x-t$ in each of the integrals. By recognizing the integral form of the gamma function $$ \Gamma(t)=\int_0^{\infty} x^{t-1} e^{-x} d x, \quad t>0, $$ and using the property of the gamma function expressed as $$ \Gamma(n+1)=n ! \quad n=0,1, \ldots, $$ deduce that $$ \int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x=e^{-\mu t} \sum_{k=0}^n \frac{(\mu t)^k}{k !}, $$ for $n=0,1, \ldots, K-1$.]

   Show that
$$
W[t]=1-\sum_{n=0}^{K-1} q_n\left(e^{-\mu t} \sum_{k=0}^n \frac{(\mu t)^k}{k !}\right)
$$
for the $\mathrm{M} / \mathrm{M} / 1 / \mathrm{K}$ queueing system, where
$$
q_n=\frac{p_n}{1-p_K}
$$
[Hint: Write
$$
\begin{aligned}
W[t] & =\sum_{n=0}^{K-1}\left\{\int_0^t \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x\right\} q_n \\
& =\sum_{n=0}^{K-1}\left\{1-\int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x\right\} q_n \\
& =1-\sum_{n=0}^{K-1} q_n \int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x .
\end{aligned}
$$
Then make the change of variable $y=x-t$ in each of the integrals. By recognizing the integral form of the gamma function
$$
\Gamma(t)=\int_0^{\infty} x^{t-1} e^{-x} d x, \quad t>0,
$$
and using the property of the gamma function expressed as
$$
\Gamma(n+1)=n ! \quad n=0,1, \ldots,
$$
deduce that
$$
\int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x=e^{-\mu t} \sum_{k=0}^n \frac{(\mu t)^k}{k !},
$$
for $n=0,1, \ldots, K-1$.]
Show more…
Probability, Statistics, and Queuing Theory with Computer Science Applications, Second Edition (Computer Science and Scientific Computing)
Probability, Statistics, and Queuing Theory with Computer Science Applications, Second Edition (Computer Science and Scientific Computing)
Arnold O. Allen 2nd Edition
Chapter 5, Problem 13 ↓

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The state probabilities $p_n$ represent the steady-state probability of having $n$ customers in the system, and $q_n$ is defined as the normalized probability of having $n$ customers, excluding the state when the system is full, i.e., $q_n = \frac{p_n}{1-p_K}$.  Show more…

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Show that $$ W[t]=1-\sum_{n=0}^{K-1} q_n\left(e^{-\mu t} \sum_{k=0}^n \frac{(\mu t)^k}{k !}\right) $$ for the $\mathrm{M} / \mathrm{M} / 1 / \mathrm{K}$ queueing system, where $$ q_n=\frac{p_n}{1-p_K} $$ [Hint: Write $$ \begin{aligned} W[t] & =\sum_{n=0}^{K-1}\left\{\int_0^t \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x\right\} q_n \\ & =\sum_{n=0}^{K-1}\left\{1-\int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x\right\} q_n \\ & =1-\sum_{n=0}^{K-1} q_n \int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x . \end{aligned} $$ Then make the change of variable $y=x-t$ in each of the integrals. By recognizing the integral form of the gamma function $$ \Gamma(t)=\int_0^{\infty} x^{t-1} e^{-x} d x, \quad t>0, $$ and using the property of the gamma function expressed as $$ \Gamma(n+1)=n ! \quad n=0,1, \ldots, $$ deduce that $$ \int_t^{\infty} \frac{\mu(\mu x)^n e^{-\mu x}}{n !} d x=e^{-\mu t} \sum_{k=0}^n \frac{(\mu t)^k}{k !}, $$ for $n=0,1, \ldots, K-1$.]
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Key Concepts

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M/M/1/K Queueing System
This concept refers to a queueing model where arrivals and service processes are both memoryless (Markovian), there is a single server, and the system has a finite capacity of K customers. In such systems, arriving customers are lost if the maximum capacity is reached, necessitating special treatment in the probability distributions and performance metrics of the system.
Exponential Distribution
The exponential distribution is pivotal in queueing theory as it characterizes the time between arrivals or the service times in a Markovian model. Its memoryless property simplifies the analysis and integration over time, as the probability density function and cumulative distribution function often appear in computations of waiting times and other performance measures.
Gamma Function
The gamma function is a generalization of the factorial function to real (and complex) numbers, defined by an integral that converges for all positive values. In queueing theory problems, the gamma function is used to evaluate integrals involving powers of the variable multiplied by an exponential function, particularly when dealing with the sum of exponential waiting times.
Incomplete Gamma Function (Cumulative Distribution Function Representation)
The incomplete gamma function arises when integrating the exponential density function from a certain point onward and is related to the cumulative distribution function of the gamma distribution. In this context, expressing the waiting time distribution involves summing terms that are representable as the incomplete gamma function, thereby tying the solution to a well-known mathematical function.
Change of Variables in Integration
This mathematical technique simplifies complex integrals by substituting a new variable for the original one, enabling the integral to be expressed in a more manageable form. In the derivation, changing the variable of integration allows the transformation of the integral into a standard form that involves the gamma function, facilitating the calculation of the cumulative distribution function.
Probability Conditioning and Normalization
In the analysis of queues with a finite capacity, probabilities must be conditioned to account for the fact that the system cannot exceed a certain number of customers. Normalizing probabilities (as seen with the q_n values) involves dividing by the complementary probability of the system being full, ensuring that the probabilities remain valid within the constrained system.

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