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dc.contributor.advisorWoodward, Mike E.
dc.contributor.authorAlghamdi, Turki A.*
dc.date.accessioned2012-04-19T14:38:06Z
dc.date.available2012-04-19T14:38:06Z
dc.date.issued2012-04-19
dc.identifier.urihttp://hdl.handle.net/10454/5420
dc.description.abstractThe growing demand on the variety of internet applications requires management of large scale networks by efficient Quality of Service (QoS) routing, which considerably contributes to the QoS architecture. The biggest contemporary drawback in the maintenance and distribution of the global state is the increase in communication overheads. Unbalancing in the network, due to the frequent use of the links assigned to the shortest path retaining most of the network loads is regarded as a major problem for best effort service. Localised QoS routing, where the source nodes use statistics collected locally, is already described in contemporary sources as more advantageous. Scalability, however, is still one of the main concerns of existing localised QoS routing algorithms. The main aim of this thesis is to present and validate new localised algorithms in order to develop the scalability of QoS routing. Existing localised routing, Credit Based Routing (CBR) and Proportional Sticky Routing (PSR), use the blocking probability as a factor in selecting the routing paths and work with either credit or flow proportion respectively, which makes impossible having up-to-date information. Therefore our proposed Highest Minimum Bandwidth (HMB) and Highest Average Bottleneck Bandwidth History (HABBH) algorithms utilise bandwidth as the direct QoS criterion to select routing paths. We introduce an Integrated Delay Based Routing and Admission Control mechanism. Using this technique Minimum Total Delay (MTD), Low Fraction Failure (LFF) and Low Path Failure (LPF) were compared against the global QoS routing scheme, Dijkstra, and localised High Path Credit (HPC) scheme and showed superior performance. The simulation with the non-uniformly distributed traffic reduced blocking probability of the proposed algorithms. Therefore, we advocate the algorithms presented in the thesis, as a scalable approach to control large networks. We strongly suggest that bandwidth and mean delay are feasible QoS constraints to select optimal paths by locally collected information. We have demonstrated that a few good candidate paths can be selected to balance the load in the network and minimise communication overhead by applying the disjoint paths method, recalculation of candidate paths set and dynamic paths selection method. Thus, localised QoS routing can be used as a load balancing tool in order to improve the network resource utilization. A delay and bandwidth combination is one of the future prospects of our work, and the positive results presented in the thesis suggest that further development of a distributed approach in candidate paths selection may enhance the proposed localised algorithms.en_US
dc.description.sponsorshipUmm AlQura University in Meccaen_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectQuality of Service routing algorithmsen_US
dc.subjectPerformance evaluationen_US
dc.subjectBandwidthen_US
dc.subjectDelayen_US
dc.subjectInternet applicationsen_US
dc.subjectQuality of Service (QoS)en_US
dc.subjectLocalised QoS routingen_US
dc.subjectScalabilityen_US
dc.titleNovel localised quality of service routing algorithms. Performance evaluation of some new localised quality of service routing algorithms based on bandwidth and delay as the metrics for candidate path selection.en_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentSchool of Computing, Informatics and Mediaen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2010
refterms.dateFOA2018-07-19T10:50:14Z


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