Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.557859
Title: Memory management architecture for next generation networks traffic managers
Author: Zhang, Q.
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2012
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Abstract:
The trend of moving conventionallP networks towards Next Generation Networks (NGNs) has highlighted the need for more sophisticated Traffic Managers (TMs) to guarantee better network and Quality of Service (QoS); these have to be scalable to support increasing link bandwidth and to cater for more diverse emerging applications. Current TM solutions though, are limited and not flexible enough to support new TM functionality or QoS with increasing diversity at faster speeds. This thesis investigates efficient and flexible memory management architectures that are critical in determining scalability and upper limits of TM performance. The approach presented takes advantage of current FPGA technology that now offers a high density of computational resources and flexible memory configurations, leading to what the author contends to be an ideal, programmable platform for distributed network management. The thesis begins with a survey of current TM solutions and their underlying technologies/architectures, the outcome of which indicates that memory and memory interfacing are the major factors in determining the scalability and upper limits of TM performance. An analysis of the implementation cost for a new TM with the capability of integrated queuing and scheduling further highlights the need to develop a more effective memory management architecture. A new on-demand QM architecture for programmable TM is then proposed that can dynamically map the ongoing active flows to a limited number of physical queues. Compared to the traditional QMs, it consumes much less memory resources, leading to a more scalable and effiCient TM solution. Based on the analysis of the effect of varying Internet traffic on the proposed OM, a more robust and resilient QM architecture is derived that achieves higher scalability and pefformance by adapting its functionality to the changing network conditions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.557859  DOI: Not available
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