Some questions in Queueing Theory

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In summary: The behaviour of the actual and effective queues will differ.In summary, the offered load for an M/M/1/FCFS/c/∞ queue is equal to the expected number in the system, or ρ=λ/μ, where λ is the average arrival rate and μ is the average service time. However, for a queue with a finite capacity, the effective load is based on the effective arrival rate, which discounts arrivals when the queue is full. This can be calculated using the formula λ(1-P(c)), where P(c) is the probability that the queue is full. Care must be taken when using this formula, as it may not accurately represent the behavior of a finite capacity queue compared to an infinite capacity queue
  • #1
sigh1342
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Homework Statement


In$$ M/M/1/FCFS/c/\infty $$
I don't know what is offered load and effective load.
Wiki say offered load is equal to the expected number in the system, and I found offered load is equal to the ρ=λ/μ, where λ is the average arrive rate. And the μ is the average service time. And I don't know which one is true , and I can't find the information about effective load.
Thank you . :^)

Homework Equations





The Attempt at a Solution

 
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  • #2
sigh1342 said:

Homework Statement


In$$ M/M/1/FCFS/c/\infty $$
I don't know what is offered load and effective load.
Wiki say offered load is equal to the expected number in the system, and I found offered load is equal to the ρ=λ/μ, where λ is the average arrive rate. And the μ is the average service time. And I don't know which one is true , and I can't find the information about effective load.
Thank you . :^)

Homework Equations





The Attempt at a Solution



Please clarify: some authors use the notation A/B/C/D?E/F is slightly different order, so you need to tell us what the ##c## stands for. My guess is that you have an infinite calling population but a finite queue capacity; is that correct?

You need to show your work; it is not enough to just say you don't know what to do. In particluar, if the 'c' means that a total of c customers can be accommodated (one in service and c-1 waiting) then some 'arriving' customers will not enter the system because it is full. In particular, you need to be careful when using such results as ##L = \bar{\lambda} W,## etc.
 
  • #3
I believe the definition of offered load is mean arrival rate * mean service time, so λ/μ. Looks to me that for a queue of finite capacity the effective load is based on the effective arrival rate, which discounts arrivals when the queue is full. See e.g. http://www.engr.sjsu.edu/udlpms/ISE 265/set4 queuing theory.ppt.
However, care must be taken in using this. You can't simply treat a queue of limited capacity as being an infinite queue with a reduced offered load.
 

1. What is queueing theory?

Queueing theory is a branch of mathematics that studies the behavior of waiting lines or queues. It provides a mathematical framework for analyzing and optimizing systems that involve waiting, such as customer service lines, traffic, and computer networks.

2. What are the key components of a queueing system?

A queueing system typically consists of three main components: arrivals, service, and queue. Arrivals refer to the customers or entities that enter the system, while service refers to the time it takes to serve each arrival. The queue is the line of arrivals waiting to be served.

3. What are some real-life applications of queueing theory?

Queueing theory has many practical applications in various industries, including transportation, healthcare, telecommunications, and manufacturing. Examples include predicting customer waiting times in call centers, optimizing traffic flow on highways, and improving patient flow in hospitals.

4. What are the common performance metrics used in queueing theory?

The most commonly used performance metrics in queueing theory are the average waiting time, the average queue length, and the average service time. Other metrics may include the utilization rate, the probability of an arrival finding the system busy, and the probability of an arrival having to wait in the queue.

5. How can queueing theory be used to improve system efficiency?

By analyzing and optimizing the key components of a queueing system, such as arrival rates, service times, and queue lengths, queueing theory can help improve system efficiency. It can also identify potential bottlenecks and suggest ways to reduce waiting times and increase service capacity.

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