Browsing by Author "McClean, S."

Now showing 1 - 6 of 6
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    Adaptive Measurement-Based Policy-Driven QoS Management with Fuzzy-Rule-based Resource Allocation
    (MDPI, 2012-07-04) Yerima, Suleiman; Parr, G.; Morrow, P.; McClean, S.
    Fixed and wireless networks are increasingly converging towards common connectivity with IP-based core networks. Providing effective end-to-end resource and QoS management in such complex heterogeneous converged network scenarios requires unified, adaptive and scalable solutions to integrate and co-ordinate diverse QoS mechanisms of different access technologies with IP-based QoS. Policy-Based Network Management (PBNM) is one approach that could be employed to address this challenge. Hence, a policy-based framework for end-to-end QoS management in converged networks, CNQF (Converged Networks QoS Management Framework) has been proposed within our project. In this paper, the CNQF architecture, a Java implementation of its prototype and experimental validation of key elements are discussed. We then present a fuzzy-based CNQF resource management approach and study the performance of our implementation with real traffic flows on an experimental testbed. The results demonstrate the efficacy of our resource-adaptive approach for practical PBNM systems.
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    Design and implementation of a measurement-based policy-driven resource management framework for converged networks
    (2011-06) Yerima, Suleiman; Parr, G.; McClean, S.; Morrow, P.; Sivalingam, K.
    This paper presents the design and implementation of a measurement-based QoS and resource management framework, CNQF (Converged Networks’ QoS Management Framework). CNQF is designed to provide unified, scalable QoS control and resource management through the use of a policy-based network management paradigm. It achieves this via distributed functional entities that are deployed to co-ordinate the resources of the transport network through centralized policy-driven decisions supported by measurement-based control architecture. We present the CNQF architecture, implementation of the prototype and validation of various inbuilt QoS control mechanisms using real traffic flows on a Linux-based experimental test bed.
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    A framework for context-driven end-to-end QoS control in Converged Networks
    (IEEE, 2010-10) Yerima, Suleiman; Parr, G.; Peoples, C.; McClean, S.; Morrow, P.
    This paper presents a framework for context-driven policy-based QoS control and end-to-end resource management in converged next generation networks. The Converged Networks QoS Framework (CNQF) is being developed within the IU-ATC project, and comprises distributed functional entities whose instances co-ordinate the converged network infrastructure to facilitate scalable and efficient end-to-end QoS management. The CNQF design leverages aspects of TISPAN, IETF and 3GPP policy-based management architectures whilst also introducing important innovative extensions to support context-aware QoS control in converged networks. The framework architecture is presented and its functionalities and operation in specific application scenarios are described.
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    Measurement-based policy-driven QoS management in Converged Networks
    (IEEE, 2011-01) Yerima, Suleiman; Parr, G.; McClean, S.; Morrow, P.
    Policy-based management is considered an effective approach to address the challenges of resource management in large complex networks. Within the IU-ATC QoS Frameworks project, a policy-based network management framework, CNQF (Converged Networks QoS Framework) is being developed aimed at providing context-aware, end-to-end QoS control and resource management in converged next generation networks. CNQF is designed to provide homogeneous, transparent QoS control over heterogeneous access technologies by means of distributed functional entities that co-ordinate the resources of the transport network through policy-driven decisions. In this paper, we present a measurement-based evaluation of policy-driven QoS management based on CNQF architecture, with real traffic flows on an experimental testbed. A Java based implementation of the CNQF Resource Management Subsystem is deployed on the testbed and results of the experiments validate the framework operation for policy-based QoS management of real traffic flows.
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    Modelling and evaluation of a policy-based resource management framework for converged next generation networks
    (IEEE, 2011-05) Yerima, Suleiman; Parr, G.; McClean, S.; Morrow, P.
    As fixed and wireless access networks converge towards Internet Protocol based transport in next generation networks, the requirement for effective and scalable control and management solutions to address the complexity introduced by their heterogeneity becomes ever more critical. Over the years, policy-based management has emerged as a viable tool to address this challenge. Within the IU-ATC project, we are developing a policy-based framework, (Converged Networks QoS Framework, CNQF) for end-to-end QoS control and resource management in converged next generation networks. CNQF is designed to support QoS control and context-driven resource management policies across all networks (access, metro, core) on the end-to-end transport layer of converged networks. This paper describes CNQF and develops an exemplary use case scenario for policy-based resource management based on our CNQF architecture. The paper also presents the development of stochastic-analytic models to characterise and evaluate the impact of policies on operational performance. The model is used to analyse the performance of dynamic CNQF context-driven policies for admission control case study.
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    A Study of Evaluation Metrics for Recommender Algorithms
    (2008-08-27) Chen, Liming; McClean, S.; Glass, D.; Redpath, J.
    There are inherent problems with evaluating the accuracy of recommender systems. Commonly-used metrics for recommender systems depend on the number of recommendations produced and the number of hidden items withheld, making it difficult to directly compare one system with another. In this paper we compare recommender algorithms using two datasets; the standard MovieLens set and an e-commerce dataset that has implicit ratings based on browsing behaviour. We introduce a measure that aids in the comparison and show how to compare results with baseline predictions based on random recommendation selections.