Performance Analysis of Bayesian Networks-based Distributed Call Admission Control for NGN Abul Bashar, [email protected]College of Computer Engineering and Sciences Prince Mohammad Bin Fahd University Al-Khobar, KSA 31952 Detlef Nauck, [email protected]Research and Technology British Telecom, Adastral Park Ipswich, UK IP5 3RE DANMS 2012: 5 th Workshop on Distributed Autonomous Network Management Systems Gerard Parr, [email protected]Sally McClean, [email protected]Bryan Scotney, [email protected]School of Computing and Info. Engg. University of Ulster Coleraine, UK BT52 1SA
19
Embed
Performance Analysis of Bayesian Networks-based Distributed Call Admission Control for NGN Abul Bashar, [email protected] College of Computer Engineering.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Performance Analysis of Bayesian Networks-based Distributed Call Admission Control for NGN
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, 16th 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
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)
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
0 500 1000 1500 2000 2500 3000 35000
50
100
150
200
250
300
Distributed_CAC Centralised_CAC
Number of Training Cases
Par
amet
er L
earn
ing
TIm
e (m
s)
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
0 500 1000 1500 2000 2500 3000 350015
20
25
30
Distributed_CAC Centralised_CAC
Number of Training Cases
Dec
isio
n M
akin
g T
ime
(ms)
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
0 100 200 300 400 500 600 700 800 900 10000
0.2
0.4
0.6
0.8
1
No_CAC Distributed_CAC Centralised_CAC
Simulation Time (sec)
Blo
ckim
g P
rob
abil
ity
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
0 100 200 300 400 500 600 700 800 900 10000
100
200
300
400
500
600
No_CAC Distributed_CAC Centralised_CAC
Simulation TIme (sec)
Ave
rag
e P
acke
t D
elay
(m
s)
Summary
FEATURE CENTRALISED DISTRIBUTED
PREDICTION ACCURACY HIGH LOW
TRAINING TIME HIGH LOW
ONLINE SPEED SAME SAME
CALL BLOCKING HIGH LOW
QOS HIGH LOW
DANMS 2012, 16th 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.