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Performance Analysis of Bayesian Networks-based Distributed Call Admission Control for NGN

Performance Analysis of Bayesian Networks-based Distributed Call Admission Control for NGN. Abul Bashar , abashar@pmu.edu.sa College of Computer Engineering and Sciences Prince Mohammad Bin Fahd University Al- Khobar , KSA 31952 . Gerard Parr , gp.parr@ulster.ac.uk

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Performance Analysis of Bayesian Networks-based Distributed Call Admission Control for NGN

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  1. Performance Analysis of Bayesian Networks-based Distributed Call Admission Control for NGN Abul Bashar, abashar@pmu.edu.sa College of Computer Engineering and Sciences Prince Mohammad Bin Fahd University Al-Khobar, KSA 31952 Gerard Parr, gp.parr@ulster.ac.uk Sally McClean, si.mcclean@ulster.ac.uk Bryan Scotney, bw.scotney@ulster.ac.uk School of Computing and Info. Engg. University of Ulster Coleraine, UK BT52 1SA Detlef Nauck, detlef.nauck@bt.com Research and Technology British Telecom, Adastral Park Ipswich, UK IP5 3RE DANMS 2012: 5thWorkshop on Distributed Autonomous Network Management Systems

  2. Outline • Introduction & Motivation • Related Work • Proposed Approach • Implementation Details • Results and Discussion • Future Work and Conclusion DANMS 2012, 16th April 2012

  3. Motivation : NGN and its Challenges IP-based, over WDM Fixed, wireless & mobile Call Admission Control function for QoS • NGN: ITU-T recommendation, Guaranteed QoS, Converged services • Reduces: CAPEX and OPEX • Challenges: Complex, heterogeneous, unpredictable • Qos Provisioning: Call Admission Control (CAC) at network edges • Problems with existing CAC: analytically intractable, non-scalable • Machine Learning for CAC: Autonomic, Scalable and Predictive solutions • Our contribution: Distributed CAC for NGN DANMS 2012, 16th April 2012

  4. Related Work and Research Objectives Existing Approaches : ML-based CAC for various networks • Neural Networks (in CDMA Cellular networks) • Reinforcement Learning (in Wireless Cellular networks) • Support Vector Machines (in UMTS networks) • Genetic Algorithms (in Wireless Mesh Networks) • Bayesian Networks (in NGN) Drawbacks of Existing Approaches • Implemented on single network element : Stand-alone solutions • Centralisedsolutions : Multiple element solutions are not distributed • No solution concerning ML-based distributed CAC Our proposed objectives • Study pros and cons of centralised and distributed solutions • To compare ML-based Centralized and Distributed CAC approaches • Performance Analysis : Prediction Accuracy, Complexity, Speed, Call Blocking Probability and QoS provisioning DANMS 2012, 16th April 2012

  5. Centralised and Distributed CAC DANMS 2012, 16th April 2012

  6. Bayesian Network Representation • BN is a probabilistic graphical model, a mapping of physical system variables into a visual and intuitive model • Directed Acylic Graph structure : using nodes and arcs • Encodes conditional independence relation among system random variables • Defined mathematically using joint probability distribution formulation • Inference feature : Repeated use of Baye’s rule to estimate unobserved nodes based on evidence of observed nodes DANMS 2012, 16th April 2012

  7. Basic theory of BN-based CAC • CAC is generally implemented at network edges • Input • Traffic Descriptors (Peak rate, Average rate, Burst duration, Service Class) • Qos Metrics (Packet Loss, Delay, Jitter) • System State (Link Bandwidth, Buffer occupancy) • Output • Admission Decision (Admit or Reject) • Estimation of Qos Metrics (Packet Loss, Delay, Jitter) • Operation • Trained offline and then used for online decision-making • Key Performance measure: Prediction accuracy, Model complexity, Speed, Blocking Prob. & QoS metrics BN-based CAC Framework on a Single Link DANMS 2012, 16th April 2012

  8. Distributed Bayesian Network Formulation BNDAC Framework for Multiple Routers BN Models • Multiple edge router topology for distributed CAC study • Three edge router pairs (IR0-ER0, IR1-ER1 and IR2-ER2) • Three BN models for each pair (BN0, BN1 and BN2) DANMS 2012, 16th April 2012

  9. BNDAC Algorithms Offline Online DANMS 2012, 16th April 2012

  10. Experimental Setup Details Network Topology in OPNET Topology definition Source Traffic definition BN Nodes Definition DANMS 2012, 16th April 2012

  11. Offline Simulation Results : Prediction Accuracy Delay Prediction Accuracy Comparison Centralised_CAC has about 11%more prediction accuracy as compared to the Distributed_CAC Reason: Centralised model has global system knowledge & hence provides accurate decisions. Distributed models provide local optimal solution. DANMS 2012, 16th April 2012

  12. Simulation Results : Implementation Complexity (1) Structure Learning Time Comparison Centralised_CAC takes about 75% more time (3000 cases) to learn the structure as compared to the Distributed_CAC Reason: Centralised model has to learn more BN nodes and their relationships (i.e more data) DANMS 2012, 16th April 2012

  13. Simulation Results : Implementation Complexity (2) Parameter Learning Time Comparison Centralised_CAC takes about 92% more time (3000 cases) to learn the parameters as compared to the Distributed_CAC Reason: Centralised model has to learn the parameter for more BN nodes (i.e more data) DANMS 2012, 16th April 2012

  14. Online Simulation Results : Decision-Making Time Decision-Making Time Comparison Centralised_CAC has similar performance as compared to the Distributed_CAC Reason: Once the models are learnt the online decision-making time is fairly low and does not vary much with the number of training cases. DANMS 2012, 16th April 2012

  15. Online Simulation Results : Blocking Probability Blocking Probability Comparison Centralised_CAC has higher blocking probability as compared to the Distributed_CAC Reason: In centralised all call request comes to a centralised model and hence takes more time to decide. In distributed model, they make independent decisions DANMS 2012, 16th April 2012

  16. Online Simulation Results : Delay Metric Delay Metric Comparison Centralised_CAC has lesser average packet delays as compared to the Distributed_CAC Reason: In centralised CAC it admits lesser calls and hence lesser packets in the queues. The tradeoff between blocked calls and QoS, Distributed scenario is still better. DANMS 2012, 16th April 2012

  17. Summary DANMS 2012, 16th April 2012

  18. Acknowledgement The authors would like to acknowledge the support of Prince Mohammad Bin Fahd University, University of Ulster, IU-ATC and British Telecom for performing this research work. DANMS 2012, 16th April 2012

  19. THANK YOU DANMS 2012, 16th April 2012

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