Queuing Theory
from Markov Chains to Multi-Server Systems
Situations where resources are shared among users appear in a wide variety of domains, from lines at stores and toll booths to queues in telecommunication networks. The management of these shared resourcescan have direct consequences on users,whether it be waiting times or blocking probabilities.
In this course, you'll learn how to describe a queuing system statistically, how to model the random evolution of queue lengths over time and calculate key performance indicators, such as an average delay or a loss probability.
This course is aimed at engineers, students and teachers interested in network planning.
Practical coursework will be carried out using ipython notebooks on a Jupyterhub server which you will be given access to.
Student testimonial
"Great MOOC ! The videos, which are relatively short, provide a good recap on Markov chains and how they apply to queues. The quizzes work well to check if you've understood." Loïc, beta-tester
"The best MOOC on edX! I'm finishing week 2 and I've never seen that much care put in a course lab! And I love these little gotchas you put into quizzes here and there! Thank you!" rka444, learner from Session 1, February - March 2018
What you'll learn
- Characterize a queue, based on probabilistic assumptions about arrivals and service times, number of servers, buffer size and service discipline
- Describe the basics of discrete time and continuous time Markov chains
- Model simple queuing systems, e.g. M/M/1 or M/M/C/C queues, as continuous time Markov chains
- Compute key performance indicators, such as an average delay, a resource utilization rate, or a loss probability, in simple single-server or multi-server system
- Design queuing simulations with the Python language to analyze how systems with limited resources distribute them between customers
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| Rating | 4.7★ based on 13 ratings |
|---|---|
| Length | 5 weeks |
| Effort | 5 weeks, 3–4 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $49 |
| From | IMT, IMTx via edX |
| Instructors | Sandrine Vaton, Isabel Amigo, Hind Castel, Patrick Maillé, Laurent Decreusefond, Michel Marot, Thierry Chonavel |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science Mathematics |
| Tags | Computer Science Data Analysis & Statistics Math |
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What people are saying
theoretical intro followed by
This course is structured so as to provide a theoretical intro followed by a practical section.
queueing theory in action
very nicely packaged short
Very nicely packaged short course with some mildly challenging exercises on a topic I've found difficult to learn from books.
staff clearly put up
The staff clearly put up a lot of effort on the preparation and follow up.
early 2018 run
This opinion is based on the early 2018 run of the course.
my primary interest
The topics are extremely interesting with applications in Queuing Delay analysis in computer networks, which is my primary interest.
native english speakers
I found some of the queueing better explained than in a supply chain course I just took from a university of native English speakers.
pass too quickly
Better if you have some previous knowledge in statistics and stochastic processes because it pass too quickly over some related mathematical concepts.
delay analysis
discussion forums
Discussion forums are also good, and the instructors respond promptly.
lecture videos
Lecture videos are short, helping to focus on one aspect at a time, and the instructors are great at explaining the basics.
start working through m.
The course motivated me to start working through M. Harchol Balter's book, "Performance Modelling and Design of Computer Systems: Queueing Theory In Action".
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Server Key $26k
Key PA $48k
Key Administrator $51k
Product Key $53k
Bartender/Key $55k
Performance Chef $56k
Performance Coach 3 $60k
Key Sales $64k
Key Animator $82k
Key Grip 3 $91k
Performance Technologist 4 $116k
Performance Analyst (R&D) $120k
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| Rating | 4.7★ based on 13 ratings |
|---|---|
| Length | 5 weeks |
| Effort | 5 weeks, 3–4 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $49 |
| From | IMT, IMTx via edX |
| Instructors | Sandrine Vaton, Isabel Amigo, Hind Castel, Patrick Maillé, Laurent Decreusefond, Michel Marot, Thierry Chonavel |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science Mathematics |
| Tags | Computer Science Data Analysis & Statistics Math |
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