Queueing server with exponential+deterministic stages

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The discussion centers on modeling the probability density function, b(x), for a system with two parallel servers: one with an exponential service rate and the other with a deterministic rate. The first server is chosen with probability p, while the second is chosen with probability 1-p. Suggestions include using a linear combination of the individual servers' PDFs to represent the overall system. Monte-Carlo simulation is mentioned as a potential method for obtaining the PDF, but the preference is for analytical computation. The importance of double-checking results through simulation is emphasized for accuracy.
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Hi, does anyone know how to model probability density function, b(x), of a system that has two parallel servers:

-The first is selected by the customer with probability p, it has an exponential rate of \mu.
-The second server is selected by the customer with probability 1-p, it has a deterministic rate of k.
-When a customer chooses a server, another servers waits until the customer being served leaves.

If the second server were an exponential rate server, it would simply be an M/Er=2/1 system but now I don't know how to model this. Can anyone help me, please?
 
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Hey lahanadar.

Are you allowed to use Monte-Carlo simulation to simulate the process so that you get a PDF (by stipulating a large enough number of simulations)?
 
Hey lahanadar.

Are you allowed to use Monte-Carlo simulation to simulate the process so that you get a PDF (by stipulating a large enough number of simulations)?
 
Not really, I should find it by computation. I think one way could be representing the over all server pdf by linear combination of individual servers' pdfs, while first has an exponential pdf and second unit step function. Do you think this works?
 
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Have you constructed a Markovian system for your queues (if you can't use simulation)?

Even if you do it analytically, I would suggest you use simulation to double check your work and get into the habit of double checking things in this way for the future.
 
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