EEL6507sp09L15

From BoykinWiki

Jump to: navigation, search

Contents

EEL6507 Spring 2009, Lecture 15, Wednesday 2009/02/09 (Notes created by Ajay Jain)

  • Finish M/M/m
  • M/M/I/k Queue

State Space Representation

Image:MMm.png
  • Recall from previous result


P_n=P_0 \prod_{k=0}^{n-1}\frac{\lambda_k}{\mu_{k+1}},

and since we should have


\sum_{n=0}^{m} F_n = \sum_{n=0}^{k} F_n + \sum_{n=k+1}^{m} F_n,

hence,


\prod_{n=0}^{k-1}\frac{\lambda_n}{\mu_{n+1}}=\prod_{n=0}^{r-1}\frac{\lambda_n}{\mu_{n+1}} \prod_{n=r}^{k-1}\frac{\lambda_n}{\mu_{n+1}},


P_k =P_0 \prod_{n=0}^{r-1}\frac{\lambda_n}{\mu_{n+1}} \prod_{n=r}^{k-1}\frac{\lambda_n}{\mu_{n+1}},


P_k =P_r \prod_{n=r}^{k-1}\frac{\lambda_n}{\mu_{n+1}},

  • CASE 1: K < m-1

So, λk = λ,μk + 1 = (k + 1)μ


P_k=P_0 \prod_{n=0}^{k-1}\frac{\lambda_k}{\mu_{k+1}} \frac{1}{n+1},


P_k=P_0 (\frac{\lambda_k}{\mu_{k+1}})^k \frac{1}{k!},


  • CASE 2: K > m

So, λk = λ,μk + 1 = mμ


P_k=P_m \prod_{n=m}^{k-1}\frac{\lambda}{m\mu},



P_k=P_m (\frac{\lambda}{m\mu})^{k-m} ,

Where,


P_k=P_0 (\frac{\lambda}{\mu})^m \frac{1}{m!},

Solving Further with Pm value we get,


P_k=P_0 (\frac{(m)^m}{m!})(\frac{\lambda}{m\mu})^{k} ,



\sum_{n=0}^{\infin} P_n = 1,


\sum_{n=0}^{m-1} P_n +\sum_{n=m}^{\infin} P_n= 1,


1=P_0 \sum_{n=0}^{m-1}(\frac{\lambda_k}{\mu_{k+1}})^k \frac{1}{k!}+P_0 \sum_{n=0}^{m-1} (\frac{(m)^m}{m!})(\frac{\lambda}{m\mu})^{k} ,


1=P_0 \sum_{n=0}^{m-1}(\frac{\lambda_k}{\mu_{k+1}})^k \frac{1}{k!}+P_0(\frac{(m)^m}{m!})\frac {(\frac{\lambda}{m\mu})^{m}}{1-\frac{\lambda}{m\mu}} ,

Let,


\rho=\frac{\lambda}{m\mu},


1=P_0 \sum_{n=0}^{m-1}(m\rho)^k \frac{1}{k!}+P_0\frac{(m\rho)^m}{m!}\frac{1}{1-\rho} ,



P_0=\frac{1}{ \sum_{n=0}^{m-1}(m\rho)^k \frac{1}{k!}+P_0\frac{(m\rho)^m}{m!}\frac{1}{1-\rho} },

Then


Prob\left[All\ Busy \right]=\frac{\frac{(m\rho)^m}{m!}\frac{1}{1-\rho}}{ \sum_{n=0}^{m-1}(m\rho)^k \frac{1}{k!}+P_0\frac{(m\rho)^m}{m!}\frac{1}{1-\rho} }

This is ERLANG-C Formula

M/M/1/k

From previous results


P_n=P_0 \prod_{k=0}^{n-1}\frac{\lambda_k}{\mu_{k+1}},

For a M / M / 1 / k system, we define the following:

P_k = P_0 \left(\frac{\lambda}{\mu}\right)^k ,  k \le m


Pk = 0,k < m

Normalising for finding value of P0


\sum_{n=0}^{\infin} P_n = 1,


P_0 \frac{1-(\frac{\lambda}{\mu})^{k+1} }{ 1-(\frac{\lambda}{\mu})^{k+1} } =1

Let,


\rho=\frac{\lambda}{\mu},


P_0=\frac{1-\rho}{1-(\rho)^{k+1}}

So


P_n=\frac{(\rho)^n (1-\rho)}{1-(\rho)^{k+1}}

  • Probability of drop

Prob\left[Drop \right]=P_k=\frac{(\rho)^k (1-\rho)}{1-(\rho)^{k+1}}

Lecture Voice Recording

lecture_recording