M/M/S Queue, probability interpretation












0












$begingroup$


In an M/M/s queue, what does this expression mean? :



$sum_{n=0}^{s-1}{(s-n)P_n}$



Furthermore, is it possible that the following equation holds? :



$sum_{n=0}^{s-1}{(s-n)P_n} = (1-rho)s$



If so, how could I demonstrate it?



EDIT: An M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers.



Thanks,

Louis










share|cite|improve this question











$endgroup$












  • $begingroup$
    What is an M/M/S queue? What are the variables that you're using?
    $endgroup$
    – Tki Deneb
    Nov 29 '18 at 17:56










  • $begingroup$
    @TkiDeneb an M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers. Thanks for your time!
    $endgroup$
    – Louis-Philippe Noël
    Nov 29 '18 at 21:08










  • $begingroup$
    I'm afraid I can't help you then, I'm not familiar with this subject.
    $endgroup$
    – Tki Deneb
    Nov 29 '18 at 22:23
















0












$begingroup$


In an M/M/s queue, what does this expression mean? :



$sum_{n=0}^{s-1}{(s-n)P_n}$



Furthermore, is it possible that the following equation holds? :



$sum_{n=0}^{s-1}{(s-n)P_n} = (1-rho)s$



If so, how could I demonstrate it?



EDIT: An M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers.



Thanks,

Louis










share|cite|improve this question











$endgroup$












  • $begingroup$
    What is an M/M/S queue? What are the variables that you're using?
    $endgroup$
    – Tki Deneb
    Nov 29 '18 at 17:56










  • $begingroup$
    @TkiDeneb an M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers. Thanks for your time!
    $endgroup$
    – Louis-Philippe Noël
    Nov 29 '18 at 21:08










  • $begingroup$
    I'm afraid I can't help you then, I'm not familiar with this subject.
    $endgroup$
    – Tki Deneb
    Nov 29 '18 at 22:23














0












0








0





$begingroup$


In an M/M/s queue, what does this expression mean? :



$sum_{n=0}^{s-1}{(s-n)P_n}$



Furthermore, is it possible that the following equation holds? :



$sum_{n=0}^{s-1}{(s-n)P_n} = (1-rho)s$



If so, how could I demonstrate it?



EDIT: An M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers.



Thanks,

Louis










share|cite|improve this question











$endgroup$




In an M/M/s queue, what does this expression mean? :



$sum_{n=0}^{s-1}{(s-n)P_n}$



Furthermore, is it possible that the following equation holds? :



$sum_{n=0}^{s-1}{(s-n)P_n} = (1-rho)s$



If so, how could I demonstrate it?



EDIT: An M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers.



Thanks,

Louis







probability queueing-theory






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Nov 29 '18 at 22:55







Louis-Philippe Noël

















asked Nov 28 '18 at 19:25









Louis-Philippe NoëlLouis-Philippe Noël

123




123












  • $begingroup$
    What is an M/M/S queue? What are the variables that you're using?
    $endgroup$
    – Tki Deneb
    Nov 29 '18 at 17:56










  • $begingroup$
    @TkiDeneb an M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers. Thanks for your time!
    $endgroup$
    – Louis-Philippe Noël
    Nov 29 '18 at 21:08










  • $begingroup$
    I'm afraid I can't help you then, I'm not familiar with this subject.
    $endgroup$
    – Tki Deneb
    Nov 29 '18 at 22:23


















  • $begingroup$
    What is an M/M/S queue? What are the variables that you're using?
    $endgroup$
    – Tki Deneb
    Nov 29 '18 at 17:56










  • $begingroup$
    @TkiDeneb an M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers. Thanks for your time!
    $endgroup$
    – Louis-Philippe Noël
    Nov 29 '18 at 21:08










  • $begingroup$
    I'm afraid I can't help you then, I'm not familiar with this subject.
    $endgroup$
    – Tki Deneb
    Nov 29 '18 at 22:23
















$begingroup$
What is an M/M/S queue? What are the variables that you're using?
$endgroup$
– Tki Deneb
Nov 29 '18 at 17:56




$begingroup$
What is an M/M/S queue? What are the variables that you're using?
$endgroup$
– Tki Deneb
Nov 29 '18 at 17:56












$begingroup$
@TkiDeneb an M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers. Thanks for your time!
$endgroup$
– Louis-Philippe Noël
Nov 29 '18 at 21:08




$begingroup$
@TkiDeneb an M/M/S queue is a queue with Markovian (Poisson law) process for arrivals, Markovian (Exponential law) process for departures and $s$ servers. Thanks for your time!
$endgroup$
– Louis-Philippe Noël
Nov 29 '18 at 21:08












$begingroup$
I'm afraid I can't help you then, I'm not familiar with this subject.
$endgroup$
– Tki Deneb
Nov 29 '18 at 22:23




$begingroup$
I'm afraid I can't help you then, I'm not familiar with this subject.
$endgroup$
– Tki Deneb
Nov 29 '18 at 22:23










1 Answer
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$begingroup$

If $p_n$ is the stationary distribution then the first expression is the expected number of free servers.



The equation is indeed correct and this can be argued using Little’s law: the arrival rate to each server is $frac{lambda}{s}$ and the expected time in service is $frac{1}{mu}$, so the probability each server is free is $1-frac{lambda}{smu}$. The servers are identical so the the sum is just $s$ times the probability of a free server.






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    1 Answer
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    1 Answer
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    0












    $begingroup$

    If $p_n$ is the stationary distribution then the first expression is the expected number of free servers.



    The equation is indeed correct and this can be argued using Little’s law: the arrival rate to each server is $frac{lambda}{s}$ and the expected time in service is $frac{1}{mu}$, so the probability each server is free is $1-frac{lambda}{smu}$. The servers are identical so the the sum is just $s$ times the probability of a free server.






    share|cite|improve this answer









    $endgroup$


















      0












      $begingroup$

      If $p_n$ is the stationary distribution then the first expression is the expected number of free servers.



      The equation is indeed correct and this can be argued using Little’s law: the arrival rate to each server is $frac{lambda}{s}$ and the expected time in service is $frac{1}{mu}$, so the probability each server is free is $1-frac{lambda}{smu}$. The servers are identical so the the sum is just $s$ times the probability of a free server.






      share|cite|improve this answer









      $endgroup$
















        0












        0








        0





        $begingroup$

        If $p_n$ is the stationary distribution then the first expression is the expected number of free servers.



        The equation is indeed correct and this can be argued using Little’s law: the arrival rate to each server is $frac{lambda}{s}$ and the expected time in service is $frac{1}{mu}$, so the probability each server is free is $1-frac{lambda}{smu}$. The servers are identical so the the sum is just $s$ times the probability of a free server.






        share|cite|improve this answer









        $endgroup$



        If $p_n$ is the stationary distribution then the first expression is the expected number of free servers.



        The equation is indeed correct and this can be argued using Little’s law: the arrival rate to each server is $frac{lambda}{s}$ and the expected time in service is $frac{1}{mu}$, so the probability each server is free is $1-frac{lambda}{smu}$. The servers are identical so the the sum is just $s$ times the probability of a free server.







        share|cite|improve this answer












        share|cite|improve this answer



        share|cite|improve this answer










        answered Dec 2 '18 at 21:15









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