The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform

Detlef Nauck - One of the best experts on this subject based on the ideXlab platform.

  • Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

  • NOMS - Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

Abul Bashar - One of the best experts on this subject based on the ideXlab platform.

  • Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

  • NOMS - Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

Bryan Scotney - One of the best experts on this subject based on the ideXlab platform.

  • Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

  • NOMS - Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

Sally Mcclean - One of the best experts on this subject based on the ideXlab platform.

  • Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

  • NOMS - Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

Gerard Parr - One of the best experts on this subject based on the ideXlab platform.

  • Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

  • NOMS - Performance analysis of Bayesian Networks-based distributed Call Admission Control for NGN
    2012 IEEE Network Operations and Management Symposium, 2012
    Co-Authors: Abul Bashar, Gerard Parr, Sally Mcclean, Bryan Scotney, Detlef Nauck
    Abstract:

    The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a Centralised Scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.