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<https://discovery.ucl.ac.uk/id/eprint/10204124> <http://purl.org/ontology/bibo/abstract> "This thesis is an investigation into maximising throughput for optical core network\r\ndesign. To calculate the maximum achievable throughput that an optical network can\r\nsustain, an NP-hard routing optimisation problem needs to be solved and therefore\r\ndesigning the network to maximise this property is computationally difficult.\r\nBoth structural and physical properties impact the maximum achievable throughput\r\nof optical networks. Therefore, the SNR-BA generative graph model is proposed in\r\nChapter 3, to investigate how structural and physical properties affect the maximum\r\nachievable throughput of optical networks. The results showed that the networks with\r\nbetter connectivity had on average 40% lower wavelength requirements and allocated\r\nbetween 8-11% more lightpaths than the SNR-BA model. However, with path lengths\r\nbetween 95 and 215% longer than the SNR-BA networks, they achieved 30-32% less\r\nmaximum achievable throughput. This demonstrated why including physical properties\r\nwithin the design of optical networks is important.\r\nMore computationally efficient methods for calculating maximum achievable\r\nthroughput were needed for it to be included in physical topology design. Chapter 4\r\nexplores several strategies to reduce this computational complexity, including linear\r\nprogramming, geometric deep learning and graph theoretical metric correlation.\r\nResults in Chapter 4 show that the proposed graph theoretical metric, demand\r\nweighted cost, had a high inverse-linear correlation to maximum achievable\r\nthroughput and thus was chosen to be embedded within the optimisation problem as a\r\nproxy for maximum achievable throughput.\r\nChapter 5 investigates whether a proxy such as demand weighted cost can maximise\r\nthe maximum achievable throughput of optical networks. Compared to a control-set the\r\ndemand weighted cost showed to increase maximum achievable throughput of networks\r\nby up to 63% compared to the control-set. However, the lowest demand weighted cost\r\ndid not always lead to the highest maximum achievable throughput. This showed that\r\nthe objective pushes networks in the right direction, however cannot directly optimise\r\nmaximum achievable throughput. To achieve this, limiting cut theory was employed,\r\nachieving a 106% increase in maximum achievable throughput compared to the control-\r\nset and thus directly optimising maximum achievable throughput of optical networks.\r\nThe results of this work can be applied to future network design and to ensure intelligent\r\naccess to achievable capacity."^^<http://www.w3.org/2001/XMLSchema#string> .
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