Sun Weng Hong. / Thesis submitted in: October 2002. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 116-121). / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.7 / Chapter 2. --- Ant Colony Optimization (ACO) --- p.9 / Chapter 2.1 --- ACO vs. Traditional Routing --- p.10 / Chapter 2.1.1 --- Routing information --- p.10 / Chapter 2.1.2 --- Routing overhead --- p.12 / Chapter 2.1.3 --- Adaptivity and Stagnation --- p.14 / Chapter 2.2 --- Approaches to Mitigate Stagnation --- p.15 / Chapter 2.2.1 --- Pheromone control --- p.15 / Chapter 2.2.1.1 --- Evaporation: --- p.15 / Chapter 2.2.1.2 --- Aging: --- p.16 / Chapter 2.2.1.3 --- Limiting and smoothing pheromone: --- p.17 / Chapter 2.2.2 --- Pheromone-Heuristic Control --- p.18 / Chapter 2.2.3 --- Privileged Pheromone Laying --- p.19 / Chapter 2.2.4 --- Critique and Comparison --- p.21 / Chapter 2.2.4.1 --- Aging --- p.22 / Chapter 2.2.4.2 --- Limiting pheromone --- p.22 / Chapter 2.2.4.3 --- Pheromone smoothing --- p.23 / Chapter 2.2.4.4 --- Evaporation --- p.25 / Chapter 2.2.4.5 --- Privileged Pheromone Laying --- p.25 / Chapter 2.2.4.6 --- Pheromone-heuristic control --- p.26 / Chapter 2.3 --- ACO in Routing and Load Balancing --- p.27 / Chapter 2.3.1 --- Ant-based Control and Its Ramifications --- p.27 / Chapter 2.3.2 --- AntNet and Its Extensions --- p.35 / Chapter 2.3.3 --- ASGA and SynthECA --- p.40 / Chapter 3. --- Multiple Ant Colony Optimization (MACO) --- p.45 / Chapter 4. --- MACO vs. ACO --- p.51 / Chapter 4.1 --- Analysis of MACO vs. ACO --- p.53 / Chapter 5. --- Applying MACO in Load Balancing --- p.89 / Chapter 5.1 --- Applying MACO in Load-balancing --- p.89 / Chapter 5.2 --- Problem Formulation --- p.91 / Chapter 5.3 --- Types of ant in MACO --- p.93 / Chapter 5.3.1 --- Allocator. --- p.94 / Chapter 5.3.2 --- Destagnator. --- p.95 / Chapter 5.3.3 --- Deallocator. --- p.100 / Chapter 5.4 --- Global Algorithm --- p.100 / Chapter 5.5 --- Discussion of the number of ant colonies --- p.103 / Chapter 6. --- Experimental Results --- p.105 / Chapter 7. --- Conclusion --- p.114 / Chapter 8. --- References --- p.116 / Appendix A. Ants in MACO --- p.122 / Appendix B. Ants in SACO. --- p.123

Thesis (MSc (Mathematical Sciences. Computer Science))--University of Stellenbosch, 2008. / Consider a path-oriented telecommunications network where calls arrive to each route in a Poisson process. Each call brings on average a fixed number of packets that are offered to route. The packet inter-arrival times and the packet lengths are exponentially distributed. Each route can queue a finite number of packets while one packet is being transmitted. Each accepted packet/call generates an amount of revenue for the route manager. At specified time instants a route manager can acquire additional capacity (“interface capacity”) in order to carry more calls and/or the manager can acquire additional buffer space in order to carry more packets, in which cases the manager earns more revenue; alternatively a route manager can earn additional revenue by selling surplus interface capacity and/or by selling surplus buffer space to other route managers that (possibly temporarily) value it more highly. We present a method for efficiently computing the buying and the selling prices of buffer space. Moreover, we propose a bandwidth reallocation scheme capable of improving the network overall rate of earning revenue at both the call level and the packet level. Our reallocation scheme combines the Erlang price [4] and our proposed buffer space price (M/M/1/K prices) to reallocate interface capacity and buffer space among routes. The proposed scheme uses local rules and decides whether or not to adjust the interface capacity and/or the buffer space. Simulation results show that the reallocation scheme achieves good performance when applied to a fictitious network of 30-nodes and 46-links based on the geography of Europe.