Prerequisites: CS 338 (Computer Communication Networks), and either
ECE 434 (Random processes), or Math 366 (Applied probability),
or consent of instructor.

Text: The following are useful, but not required:

• Berstekas and Gallager, Data Networks, 2nd edition.
• Kleinrock, Queueing Systems, Volume 1

Lecture notes are available at TIS on Green Street

I. Issues and Models

• Network layers & architectures
• Markov & state space models
• Control

II. Introduction to Network Scheduling

• Workload & System load
• Basics of efficient scheduling
• KSRS & Klimov examples

III. Network Routing

• Workload
• Fair and efficient equilibria
• Braess' paradox
• Bellman-Ford algorithm
• Distributed algorithms

IV. Heavy Traffic Approximations

• The snap-shot principle
• Workload relaxations
• State space collapse

V. Markov Models

• Generators
• Invariance equations
• Lyapunov functions
• Limit theory

VI. The Single Queue

• The G/GI/1 queue
• Performance bounds
• Related simple models

VII. Loss Models

• Large deviations
• Effective bandwidth
• Congestion notification

VIII. Stochastic Network Models

• Markov queueing networks
• Jackson networks
• Circuit switched networks

IX. Network Stability

• Lyapunov functions
• Fluid limit models
• Multistep drift criteria

X. Network Performance

• Performance metrics
• Bounds
• Simulation

XI. Multiple Access

• Finite ALOHA model
• Infinite-station model
• Probing algorithms

XII. Optimization of Markov Models

• Optimality equations
• Dynamic programming
• Congested networks

XIII. Scheduling Revisited

• Safety stocks
• Stability
• Heavy traffic