The University of Sheffield
School of Computer Science

COM4515 Network Performance Analysis

Summary This module considers the performance of computer networks from a statistical aspect, using queuing theory. It is shown that the performance of a computer network depends heavily on the traffic flow in the network, and different models of traffic and queues are used. These include single-server queues, multiple server queues, and the concept of blocking is discussed. Although the analysis is entirely statistical, all the relevant background is provided in the lectures, such that the course is entirely self-contained. Problem sheets are provided in order to assist the students with the course material.
Session Spring 2025/26
Credits 15
Assessment Formal examination [100%] (2 hours)
Lecturer(s) Dr Joab Winkler
Resources
Aims
  • Introduce a statistical analysis of computer networks
  • Show how queuing theory can be used to analyse computer networks
  • Show the applications of queuing theory to other areas of science and engineering
Learning Outcomes  By the end of the unit the candidate should be able to:
  • Analyse queuing networks and their application to computer networks
  • Show how to design a computer network so that the queues are within specified bounds
  • Appreciate the importance of queuing theory to other areas of engineering
Content
  • Review material:
    • Networking Introduction.
    • Random processes and probability theory.
    • The Poisson distribution
  • A simple network queue:
    • M/M/1 queue
    • Birth death processes
    • Little's formula
  • Richer queueing models:
    • M/M/M/1.
    • Queues in which the arrival and service rates are functions of the state system
    • Queues with blocking
    • Erlang delay.
    • Erlang loss system.
    • Erlang B and C curves.
    • M/G/1.
  • Overview of Simulation:
    • Sampling theory.
    • Obtaining samples from Markov chains.
Restrictions  Optional modules within the school have limited capacity. We will always try to accommodate all students but cannot guarantee a place. 
Essential skills The course requires basic probability theory and knowledge of arithmetic and geometric progressions. This knowledge is basic and it is covered in all undergraduate engineering and physics degrees.
Teaching Method The teaching method is by lectures with numerous examples and problem sheets so that the students have a good theoretical and practical understanding of the subject.
  • 34 hours of formal lectures, which includes problem classes.
Feedback Problem sheets are set and discussed in class and this provides an opportunity for feedback.