Network Design and Optimisation

This subject has the following teaching availabilities in 2015:

200 hours

4-year Electrical Engineering degree or equivalent.

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For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/

AIMS

This subject provides knowledge and skills necessary for designing and optimization of telecommunication network.

INDICATIVE CONTENT

Topics in this subject can be generically applied to wired or wireless networks and are not limited to any specific type, tier or networking layer. More specifically, the subject will include:

• Topological modelling of telecommunication network
• Capacity planning and design; problems involving flow
• Content and data delivery; supply and demand in telecommunication networks
• Network cost optimization with flow considerations
• Ethernet and its application, collision detection; spanning trees
• Routing protocols; shortest path problems
• Application of evolutionary computation in network design and optimization
• Quality of service and class of service (core network); Multiprotocole Label Switching
• Designing for performance, consideration of service level agreements in network design
• Survivability, reliability and availability in network design; Designing fault tolerant network; Self healing design techniques; Fault detection mechanisms
• Packet loss, delay and buffer size consideration in network design; Application of relevant queuing models

INTENDED LEARNING OUTCOMES (ILO)

On completion of the subject, students will be equipped with a strong background in application of modelling and analytical techniques to design and optimise practical networking problems. Specifically, it is expected that students acquire the following set of skills and knowledge:

1. Linear programming formulation of network design and optimization problem; Simplex algorithm
2. Maximum flow problem; Path augmentation and labelling methods Transportation problem; Minimum cost and penalty cost method for finding feasible solution; Modified distribution method for finding minimum cost supply-demand solution
3. Minimum cost flow problem; Network simplex method
4. Prim’s and Kruskal’s algorithm for minimum spanning trees
5. Shortest math problem; Dijkstra algorithm
6. Travelling sales man problem; application of branch and bound
7. Application of Genetic algorithm, Tabu search and hill climbing in network design and optimization (including cost optimization)
8. Modelling network redundancies; cost consideration of adding redundancies (as a multiobjective optimization example)
9. Obtaining availability and reliability figures; application of mean time to failure and mean time to repair and the relevant formulations
10. Little’s formula, Deterministic queuing models; Single server Markov chain models such as M/M/k, M/M/k/k, finite buffer, finite source, state dependent and Markov modulated models; G/G/1 and priority queuing models; Internet traffic models, queuing networks, and telecommunication applications; Recursion of Erlang B and Engset formula

• Continuous assessment of submitted project work completed in small groups (2-3 students), not exceeding 30 pages in total, due in week 12 (approximately 55-60 hours of work per student), 40%;
• Mid semester test of one and half hour duration, 10%;
• One written examination, not exceeding three hours at the end of semester 50%.

Hurdle requirement: Students must pass the written exam to pass the subject.

Intended Learning Outcomes (ILOs) 1-10 are assessed in the final exam, the mid- semester test, and the submitted project report and presentation.

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Additional Reading:

• A. Kershenbaum, "Telecommunications Network Design Algorithms", McGraw Hill International Additions, Computer Science Series, 1993. ISBN: 0-07-112518-3.

• K.G. Murty, "Linear Combinational Programming", John Wiley and Sons, Inc., New York, 1976.

• Other current sources will be recommended on the subject website

This subject is not available as a breadth subject.

On completion of this subject, the students should have developed:

• Problem solving and analytical skills;
• Critical and creative thinking, with an aptitude for continued self-directed learning;
• Sense of intellectual curiosity;
• Ability to interpret data and research results;
• Ability to learn in a range of ways, including through information and communication technologies;
• Capacity to confront unfamiliar problems;
• Ability to evaluate and synthesise the research and professional literature;
• Ability to develop models of practical applications and evaluate their performance by rigorous analytical means.

LEARNING AND TEACHING METHODS

The subject is delivered through sessions that combine lecture presentation, discussion, and mini-tutorials. Private study is also required, in addition to the weekly sessions. Learning is also enhanced by active participation in the online Discussion Board.

INDICATIVE KEY LEARNING RESOURCES

Students are provided with lecture slides and tutorial problems. Extensive reference material is uploaded or linked on the subject website.

CAREERS / INDUSTRY LINKS

Student teams are encouraged to interact with industry professionals as part of their assignment. Specific industry interactions may be organised according to interest and opportunities.