Performance Analysis of Bayesian Networks-based Distributed Call Admission Control for NGN Abul Bashar, College of Computer Engineering and Sciences Prince Mohammad Bin Fahd University Al-Khobar, KSA Detlef Nauck, Research and Technology British Telecom, Adastral Park Ipswich, UK IP5 3RE DANMS 2012: 5 th Workshop on Distributed Autonomous Network Management Systems Gerard Parr, Sally McClean, Bryan Scotney, School of Computing and Info. Engg. University of Ulster Coleraine, UK BT52 1SA

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

Motivation : NGN and its Challenges IP-based, over WDM  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 Fixed, wireless & mobile Call Admission Control function for QoS DANMS 2012, 16 th April 2012

Related Work and Research Objectives  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) Existing Approaches : ML-based CAC for various networks 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 Drawbacks of Existing Approaches  Implemented on single network element : Stand-alone solutions  Centralised solutions : Multiple element solutions are not distributed  No solution concerning ML-based distributed CAC DANMS 2012, 16 th April 2012

Centralised and Distributed CAC DANMS 2012, 16 th April 2012

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, 16 th April 2012

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, 16 th April 2012

Distributed Bayesian Network Formulation  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, 16 th April 2012 BNDAC Framework for Multiple RoutersBN Models

BNDAC Algorithms DANMS 2012, 16 th April 2012 Online Offline

Experimental Setup Details ParameterValue SourcesS0, S1, S2 DestinationsD0, D1, D2 Ingress RoutersIR0, IR1, IR2 Egress RoutersER0, ER1, ER2 Core RoutersCR0, CR1, CR2 ParameterValue Flow generation rate (flows/sec) 5 Average flow duration (sec)2.0 Packet generation rate (packets/sec)Exponential (4) Packet size (bits)Exponential (1024) Type of service Expedited Forwarding Topology definition Source Traffic definition Network Topology in OPNET BN NodeDescription Traffic Incoming Traffic QueueQueue Size DelayE2E Packet Delay LossLost Packets BN Nodes Definition DANMS 2012, 16 th April 2012

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, 16 th April 2012

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, 16 th April 2012

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, 16 th April 2012

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, 16 th April 2012

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, 16 th April 2012

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, 16 th April 2012

Summary FEATURECENTRALISEDDISTRIBUTED PREDICTION ACCURACY HIGHLOW TRAINING TIME HIGHLOW ONLINE SPEED SAME CALL BLOCKING HIGHLOW QOS HIGHLOW DANMS 2012, 16 th April 2012

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, 16 th April 2012

THANK YOU DANMS 2012, 16 th April 2012