Network Analysis and Dimensioning

Dublin City University

Course Description

  • Course Name

    Network Analysis and Dimensioning

  • Host University

    Dublin City University

  • Location

    Dublin, Ireland

  • Area of Study

    Electronics Engineering

  • Language Level

    Taught In English

  • Course Level Recommendations


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    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    The aim of this module is to introduce the theory and practice of mathematical network analysis and optimisation methods as they apply to the problems of performance analysis of communications protocols, network dimensioning and capacity planning, network architecture design and traffic analysis in modern large-scale data networks, such as optically switched metro and access networks, datacenter and high performance computing interconnects, and femto-macro cell wireless network architectures. Network analysis is essential to understanding and evaluating the fundamental performance properties (e.g. latency, jitter, throughput, packet-drop rate) of complex network architectures and communications protocols. Network dimensioning methods are essential to planning and deploying large-scale networks under given capacity and cost constraints. This module will cover fundamental theory in probability, stochastic processes, queuing theory, graph theory and optimisation methods and apply them to solving various data network design and performance management problems.

    Learning Outcomes
    1. Derive key results in queuing and teletraffic theory, as apply to the study of communication network performance analysis.
    2. Apply methods from probability and queuing theory to modelling of performance-related behaviour of a range of packet-switched and circuit-switched systems and networks.
    3. Apply queuing theory equations to calculate system performance measures (e.g. latency, throughput, packet loss) and to perform basic dimensioning of network resources to meet required performance targets.
    4. Develop a number of different probabilistic traffic models and determine their applicability to representing different network traffic types.
    5. Formulate a range of different network flow and resource dimensioning problems as mathematical optimisation problems.
    6. Apply optimisation theory to solving network flow, routing and resource allocation problems.