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

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7 Νοε 2013 (πριν από 3 χρόνια και 5 μήνες)

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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

Detlef Nauck
,
detlef.nauck@bt.com


Research
and Technology

British Telecom, Adastral Park

Ipswich, UK IP5 3RE


DANMS 2012: 5
th

Workshop on Distributed Autonomous Network Management Systems

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

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

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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

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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


PHYSICAL SYSTEM
IP CORE
NETWORK
EDGE
NETWORK
ACCESS
NETWORK
ACCESS
DEVICES
SERVICE
USERS
APPLICATIONS

/
SERVICES
LAN
xDSL
SATELLITE
WIRELESS
OPTICAL
FIBER
GATEWAY
B
S
A
X
E
D
L
T
BAYESIAN NETWORK MODEL
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

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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
)

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BNDAC Framework for Multiple Routers

BN Models

BNDAC Algorithms

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Online

Offline

Experimental Setup Details

Parameter

Value

Sources

S0, S1, S2

Destinations

D0, D1, D2

Ingress Routers

IR0, IR1, IR2

Egress Routers

ER0, ER1, ER2

Core Routers

CR0, CR1, CR2

Parameter

Value

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 Node

Description

Traffic


Incoming Traffic

Queue

Queue Size

Delay

E2E Packet Delay

Loss

Lost 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

70
75
80
85
90
95
0
500
1000
1500
2000
2500
3000
3500
Prediction Accuracy (%)

Number of Training Cases

Distributed_CAC
Centralised_CAC
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

0
20
40
60
80
0
500
1000
1500
2000
2500
3000
3500
Structure Learning Time (ms)

Number of Training Cases

Distributed_CAC
Centralised_CAC
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

0
50
100
150
200
250
300
0
500
1000
1500
2000
2500
3000
3500
Parameter Learning TIme (ms)

Number of Training Cases

Distributed_CAC
Centralised_CAC
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
.

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th

April 2012

15
20
25
30
0
500
1000
1500
2000
2500
3000
3500
Decision Making Time (ms)

Number
of

Training Cases

Distributed_CAC
Centralised_CAC
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

0
0.2
0.4
0.6
0.8
1
0
100
200
300
400
500
600
700
800
900
1000
Blockimg Probability

Simulation Time (sec)

No_CAC
Distributed_CAC
Centralised_CAC
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

0
100
200
300
400
500
600
0
100
200
300
400
500
600
700
800
900
1000
Average Packet Delay (ms)

Simulation TIme (sec)

No_CAC
Distributed_CAC
Centralised_CAC
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, 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