Queuing Theory problem (M/M queue)

In summary, this conversation discusses an M/M type queuing model with two types of customers and one cashier. The arrival rates and service time distribution are given, and it is stated that type 1 customers may skip queuing if the number of customers exceeds a certain amount. The conversation then goes on to ask for the balance equation expressed with the balance state probability for n customers in the system, and how to solve this equation and find the conditions for an existing balance state. Finally, the conversation asks for the average number of customers in the system, which can be solved using Little's Formula.
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Homework Statement


Given an M/M type queuing model on an first come first save basis, where 2 kinds of customers (type 1 and type 2) arrive as poisson processes with arrival rate λ1 and λ2 respectively, being served by one cashier with timewise exponential distribution with parameter μ. However, if the amount of customers in the system (type 1 and type 2) are more than K people, then customers of type 1 can skip queing and leave the cashier. Type 2 will always queue no matter what.
(1) Give the balance equation expressed with the balance state probability for n customers in the system pn.
(2) Solve the equation from (1). Find the conditions so that there exists an balance state.
(3) Find the average number of customers in the system.

Homework Equations




The Attempt at a Solution


(1)
I think I got this one right, I made a simple graph
Numbers below show states (how many people are in the queue) and the arrows with probabilies indicate change of states (not a beautiful illustration, but hopefully you understand).
1 >-λ12-> 2 >-λ12-> ... >-λ12-> n-1 >-λ12-> n >-λ12-> n+1 >-λ12-> ... >-λ12-> K >-λ2-> ...
1 <-μ-< 2 <-μ-< ... <-μ-n<-μ-< ... <-μ-< K <-μ-< ...

This gives:
μ p1 = (λ12) p0

(μ + λ1 + λ2) pn = (λ12) pn-1 + μ pn+1 for 0 < n < K

(μ + λ2) pK = (λ12) pK-1 + μ pK+1

(μ + λ2) pn = λ2 pn-1 + μ pn+1 for n > K

And maybe also use

[itex]\sum_{n=0}^{\infty}p_{n} = 1[/itex]

(2) Solving this is where I got stuck, I was thinking about doing it with Z-transform but it didn't really work out. He seems to just see how it turns out in the other examples in the book, but I have a hard time to see how to solve this. As for the conditions for convergence, I'm a bit clueless.


(3) I think I'm supposed to do this with Little's Formula
 
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  • #2
, but I'm unsure how. I've read the wikipedia page, and some other sources, but it's still quite blurry. It would be nice if someone could explain how to do this using Little's Formula.
 

FAQ: Queuing Theory problem (M/M queue)

1. What is the M/M queue in queuing theory?

The M/M queue is a mathematical model used in queuing theory to analyze the behavior of waiting lines. It assumes that the arrival of customers follows a Poisson distribution and the service times follow an exponential distribution. This model is commonly used to study systems such as call centers, traffic flow, and computer networks.

2. What is the difference between an M/M queue and an M/M/1 queue?

An M/M queue only has one server, while an M/M/1 queue has multiple servers. This means that in an M/M/1 queue, multiple customers can be served simultaneously, whereas in an M/M queue, only one customer can be served at a time.

3. How is the average waiting time calculated in an M/M queue?

The average waiting time in an M/M queue is calculated by dividing the average number of customers in the system by the arrival rate (λ). This can be represented by the formula: W = L/λ, where W is the average waiting time and L is the average number of customers in the system.

4. What is the utilization factor in an M/M queue?

The utilization factor in an M/M queue is the ratio of the average service rate (μ) to the arrival rate (λ). It represents the percentage of time the server is busy and is often denoted by the symbol ρ. The utilization factor is an important measure in queuing theory as it indicates the efficiency of the system.

5. How is Little's Law applied in an M/M queue?

Little's Law is a fundamental principle in queuing theory that states that the average number of customers in a queuing system (L) is equal to the arrival rate (λ) multiplied by the average waiting time (W). In an M/M queue, Little's Law can be applied to calculate the average number of customers in the system, which is a key performance metric for evaluating the queuing system.

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