Browsing Department of Mathematical Sciences by browse.metadata.advisor "Bagula, Antoine B."
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- ItemIGP traffic engineering : a comparison of computational optimization algorithms(Stellenbosch : Stellenbosch University, 2008-03) Wang, Hong Feng; Bagula, Antoine B.; Krzesinski, A. E.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Institute for Applied Computer Science.ENGLISH ABSTRACT: Traffic Engineering (TE) is intended to be used in next generation IP networks to optimize the usage of network resources by effecting QoS agreements between the traffic offered to the network and the available network resources. TE is currently performed by the IP community using three methods including (1) IGP TE using connectionless routing optimization (2) MPLS TE using connection-oriented routing optimization and (3) Hybrid TE combining IGP TE with MPLS TE. MPLS has won the battle of the core of the Internet and is making its way into metro, access and even some private networks. However, emerging provider practices are revealing the relevance of using IGP TE in hybrid TE models where IGP TE is combined with MPLS TE to optimize IP routing. This is done by either optimizing IGP routing while setting a few number of MPLS tunnels in the network or optimizing the management of MPLS tunnels to allow growth for the IGP traffic or optimizing both IGP and MPLS routing in a hybrid IGP+MPLS setting. The focus of this thesis is on IGP TE using heuristic algorithms borrowed from the computational intelligence research field. We present four classes of algorithms for Maximum Link Utilization (MLU) minimization. These include Genetic Algorithm (GA), Gene Expression Programming (GEP), Ant Colony Optimization (ACO), and Simulated Annealing (SA). We use these algorithms to compute a set of optimal link weights to achieve IGP TE in different settings where a set of test networks representing Europe, USA, Africa and China are used. Using NS simulation, we compare the performance of these algorithms on the test networks with various traffic profiles.
- ItemNetwork engineering using multi-objective evolutionary algorithms(Stellenbosch : Stellenbosch University, 2007-12) Baruani, Atumbe Jules; Bagula, Antoine B.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Institute for Applied Computer Science.ENGLISH ABSTRACT: We use Evolutionary Multi-Objective Optimisation (EMOO) algorithms to optimise objective functions that reflect situations in communication networks. These include functions that optimise Network Engineering (NE) objective functions in core, metro and wireless sensor networks. The main contributions of this thesis are threefold. Routing and Wavelength Assignment (RWA) for IP backbone networks. Routing and Wavelength Assignment (RWA) is a problem that has been widely addressed by the optical research community. A recent interest in this problem has been raised by the need to achieve routing optimisation in the emerging generation multilayer networks where data networks are layered above a Dense Wavelength Division Multiplexing (DWDM) network. We formulate the RWA as both a single and a multi-objective optimisation problem which are solved using a two-step solution where (1) a set of paths are found using genetic optimisation and (2) a graph coloring approach is implemented to assign wavelengths to these paths. The experimental results from both optimisation scenarios reveal the impact of (1) the cost metric used which equivalently defines the fitness function (2) the algorithmic solution adopted and (3) the topology of the network on the performance achieved by the RWA procedure in terms of path quality and wavelength assignment. Optimisation of Arrayed Waveguide Grating (AWG) Metro Networks. An Arrayed Waveguide Grating (AWG) is a device that can be used as a multiplexer or demultiplexer in WDM systems. It can also be used as a drop-and-insert element or even a wavelength router. We take a closer look at how the hardware and software parameters of an AWG can be fine tuned in order to maximise throughput and minimise the delay. We adopt a multi-objective optimisation approach for multi-service AWG-based single hop metro WDM networks. Using a previously proposed multi-objective optimisation model as a benchmark, we propose several EMOO solutions and compare their efficiency by evaluating their impact on the performance achieved by the AWG optimisation process. Simulation reveals that (1) different EMOO algorithms can exhibit different performance patterns and (2) good network planning and operation solutions for a wide range of traffic scenarios can result from a well selected EMOO algorithm. Wireless Sensor Networks (WSNs) Topology (layout) Optimisation. WSNs have been used in a number of application areas to achieve vital functions in situations where humans cannot constantly be available for certain tasks such as in hostile areas like war zones, seismic sensing where continuous inspection and detection are needed, and many other applications such as environment monitoring, military operations and surveillance. Research and practice have shown that there is a need to optimise the topology (layout) of such sensors on the ground because the position on which they land may affect the sensing efficiency. We formulate the problem of layout optimisation as a multi-objective optimisation problem consisting of maximising both the coverage (area) and the lifetime of the wireless sensor network. We propose different algorithmic evolutionary multi-objective methods and compare their performance in terms of Pareto solutions. Simulations reveal that the Pareto solutions found lead to different performance patterns and types of layouts.
- ItemTraffic Engineering using Multipath Routing Approaches(2007-12) Mazandu, Gaston Kuzamunu; Bagula, Antoine B.; University of Stellenbosch. Faculty of Science. Dept. of Mathematical Sciences. Institute for Applied Computer Science.It is widely recognized that Traffic engineering (TE) mechanisms have to be added to the IP transport functionalities to provide QoS guarantees while ensuring efficient use of network resources. Traffic engineering is a network management technique which routes traffic to where bandwidth is available in the network to achieve QoS agreements between current and future demands and the available network resources. Multi-path routing has been proven to be a more efficient TE mechanism than Shortest Path First (SPF) routing in terms of proffit maximization and resource usage optimization. However the identiffication of set of paths over which traffic is forwarded from source to the destination and the distribution of traffic among these paths are two issues that have been widely addressed by the IP community but remain an open issue for the emerging generation IP networks. Building upon different frameworks, this thesis revisits the issue of multi-path routing to present and evaluate the performance of different traffic splitting mechanisms to achieve QoS routing in Multi-Protocol Label Switching (MPLS) and Wireless Sensor Networks (WSNs). Three main contributions are identified in this thesis. First, we extend an optimization model that used the M/M/1 queueing model on a simple network consisting of a single source-destination pair by using the M/M/s queueing model on a general network consisting of several source-destination pairs. The model solves a multi-path routing problem by defining a Hamiltonian as a function of delay incurred and subjecting this Hamiltonian to Pontryagin's cost minimization to achieve efficient diffusion of traffic over the available parallel paths. Second, we revisit the problem of cost-based optimization in a multi-path setting by using a Game theoretical framework to propose and evaluate the performance of competitive and cooperative multi-path routing schemes and the impact of the routing metric (cost) on the difference between these two schemes. Finally, building upon a previously proposed optimization benchmark, we propose an Energy constrained QoS routing scheme for Wireless Sensor Networks and show through simulation that our scheme outperforms the benchmark scheme.