• Computer Lab
• Damon Wischik
• Network Performance

Network Performance 2011/2012

This is a 24-lecture course taught to masters students and fourth-year undergraduates at UCL. I teach basic tools for modelling computer networks: looking at data, using appropriate simulations, and analysing mathematical models.

• Problem sets & past papers
• Further reading
• List of examinable material and notes on how to answer questions.

Course outline

• Section 0. Introduction

  [pptx]

• Section 1. Random numbers

  [pdf]
  §1.1 Introduction, §1.2 Visualization [R demo], §1.3 Generating random variables, §1.4 Fitting distributions, §1.5 Describing distributions, §1.6 Independence, §1.7 Working with random variables, statistical multiplexing [nb demo], §1.8 Common random variables [rv.pdf], [nb demo].

• Section 2. Simulation

  [pdf] [badsim.pdf] [badsim.py] (Python tips)
  §2.1 Types of simulator, §2.2 Where you measure from, §2.3 Steady state, §2.4 Replication, §2.5 Mice and elephants, §2.6 Standard models.

• Section 3. Analysis tools

  [pdf]
  §3.1 Drift models [R, xlsx nb], §3.2 Erlang fixed point [nb, R py], §3.3 Dynamic alternative routing [R, nb], §3.4 Operational laws.

• Section 4. TCP

  [pdf]
  §4.1 TCP algorithm [pptx], §4.2 Drift model [nb], §4.3 Fixed point [nb].

• Section 5. Resource allocation

  [pdf], [pptx]
  §5.1 Admissible region [nb], §5.2 Objective function, §5.3 Constrained optimization [nb], §5.4 TCP as optimization.

• Section 6. Conclusion

  [pdf], [pptx]

Pre-requisites.

This course assumes basic maths skills, including a working knowledge of elementary calculus (integration, differentiation, maximization) and probability. The coursework requires some programming ability; programming examples will be given in Python and R.