Studies On Traffic Management Models for Wireless Communication Network
1. Studies On Traffic
Management Models For
Wireless Communication
Network
Presented By:
Neeta Singh
Center for Information and Decision Sciences,
Dr. B.R. Ambedkar University,
Agra-282002 (India)
Visit: https://www.rdcsolution.com,
Youtube: https://youtu.be/jYBglnf4VvA
2. Studies On Traffic Management Models
For Wireless Communication Network
WLL Cellular
WCNs
VOD
application
3. Chapters
Chapter 1: Introduction
Chapter 2: Some Markov Models
Chapter 3: Performance Analysis Of WLL
Chapter 4: Multilayer Cellular Network
Chapter 5: Performance Analysis Of Cellular Radio System Using
Soft-Computing Approach
Chapter 6: Performance Analysis Of Video Servers
Chapter 7: Servicing Schemes In Video-On-Demand
4. 1.1: Motivation
1.2: Performance Modeling of WCNs
1.3: Traffic Management in WCNs
1.4: Some Perspectives of WCNs
1.5: Methodological Aspects
1.6: Performance Measures
Chapter 1: Introduction
5. Motivation
The popularity of WCNs is due to several
advantages
The latter advantage enables what is often
called the 3A paradigm communications
Ultimate goal of wireless communication is to
ensure information exchange (Voice,
Data/Video) with any one, any where and at
any time.
6. Performance is one of the important issues that
effects the design, development, configuration and
tuning of a wireless communication system.
The performance analysis of WCNs can be done by
evaluating various performance measures viz.
throughput, delay, carried load, queue length,
blocking probability of traffic in the network, etc.
Thus performance analysis is needed throughout the
life cycle of such system.
Performance Modeling of WCNs
7. Traffic management is the art of providing users with
the service they need and have paid for.
An additional goal of traffic management is to make
efficient use of network resources.
The amount of traffic capacity required in a wireless
network is highly dependent on the type of traffic.
It appears that the integration of traffic voice, images,
data and video, each with its own multi objective QoS
requires the development of rather sophisticated
traffic models to carry out accurate design and
performance evaluation.
Traffic Management
9. Performance Measures
Blocking probability (new/handoff)
Delay probability
Dropping probability
Utilization
Average waiting time
Average number of calls
Average number of calls in the system
Response time in the system
Queue size distribution
Throughput/ Utilization
Traffic delay
10. Some Perspectives of WCNs
Public
Switched
Telephone
Network
MTSO
BTS
BTS
BTS
BTS
MTSO: Mobile Telecommunication Switching Office
Also known as MSC (Mobile Switching Center)
BTS: Base Transceiver Station
11. Call from conventional telephone
Call arrives at MSC via the PSTN
MSC then sends out a paging message via all BTS on
the FCC (Forward Control Channel).
The paging message contains subscriber’s Mobile
Identification Number (MIN)
The mobile unit responds with an acknowledgement on
the RCC (Reverse Control Channel)
MSC directs BS to assign FVC (Forward Voice
Channel) and RVC (Reverse Voice Channel)
BS also assigns a SAT tone (Supervisory Audio Tone)
and the VMAC (Voice Mobile Attenuation Code)
14. Chapter 2
M/M/1 Queue With Working Vacation And
Service Interruption (Published in CSI)
Markovian Queue With Randomly Changing
Arrival Rates Under N-Policy
Optimal Policy for Retrial Single Server
Queue With Reneging
back
15. Section 2A
What is Working Vacation ?
The free server may offer service to the customer, which are
not registered previously in normal case.
The free server is called on ‘working vacation’ as it can hear
the request of other type traffic via control distributed channel
and returns back after a random interval to establish its normal
connection, if there is any packet of routine type to be
transmitted in the buffer.
Systems is based on:
Unreliable Server
Breakdown
Repair
Setup
Techniques
Matrix geometric
back
16. Model Description
P0,0 P1,0 P2,0 P3,0 P4,0 Pn,0
l0
l0 l0 l0 l0 l0
mv mv mv mv mv mv
P1,1 P2,1 P3,1 P4,1 Pn,1
l1 l1 l1 l1 l1
mB mB mB mB mB
0 0 0
P1,2 P2,2 P3,2 P4,2 Pn,2
l2 l2 l2 l2 l2
P1,3 P2,3 P3,3 P4,3 Pn,3
l3 l3 l3 l3 l3
0 0 0
0 0 0
0 0 0
mv
h h h h h
a a a a a
q q q q q
b b b b b
l3
l1
l2
l0
mB
Busy state
Down state
Repair state
Vacation
state
0 0 0
0 0 0
0 0 0
0 0 0
state
customers
back
17. Probabilities
P(n,0) Probability of n customers being in the
system when the server is on working
vacation.
P(n,1) Probability of n customers being in the
system when the server is busy.
P(n,2) Probability of n customers being in the
system when the server is under
breakdown state.
P(n,3) Probability of n customers being in the
system when the server is under repair
state.
back
18. Performance Measures
The average number of customers in the
working vacation period:
The average number of customers in the system
when the server is in the busy state:
The average number of customers in the system
when the server is in broken down state:
0
)0,()(
n
nnPVE
1
)1,()(
n
nnPBE
1
)2,()(
n
nnPDE
back
19. Cont…
The average number of customers in the
working vacation period
The average number of customers in the
busy state
The average number of customers in the
broken down state
0
)0,()(
n
nnPVE
1
)1,()(
n
nnPBE
1
)2,()(
n
nnPDE
back
20. The average number of customers in the
repair state
The average number of customers in the
system
Throughput
Average delay
1
)3,()(
n
nnPRE
)()()()()( REDEBEVENE
1
)1,(
1
)0,(
n
nB
n
nv pp mm
)(NE
D
Numerical Result back
21. Concluding Remark
M/M/1 queueing model
unreliable server , working vacation and setup
time.
state-dependent arrival rate
We have facilitated sensitivity analysis which
can be further used to determine the optimal
control parameter.
back
22. Section: 2B
What is Randomly Changing Arrival Rates ?
A communication network system is the typical example of
changing arrival rate with a sudden increase in the traffic due
to an external phenomenon.
Systems is based on:
Two Channel
Random Intensity
N-Policy
Techniques
Generating function,
Matrix geometric
back
24. Steady State Probabilities
Pn,0 Probability that channel P is in Idle state and there are
‘n’ number of messages in the queue in front of P
Pn,1 Probability that channel P is in Busy state and there
are ‘n’ number of messages in the queue including in
service in front of P
Qn,0 Probability that channel Q is in Idle state and there are
‘n’ number of messages in the queue in front of Q
Qn,1 Probability that channel Q is in Busy state and there
are ‘n’ number of messages in the queue including in
service in front of Q
back
25. 1 2 n N-1 N N+1
0 1
0 1 N-1
pl0 pl0 pl0pl0
m0
m0
m0
m0 m0 m0 m0 m0
pl0 pl0
pl0 pl0 pl0
rl1 rl1 rl1 rl1 rl1rl1 rl1
m1 m1
m1 m1 m1m1 m1
rl1
rl1 rl1
m1
2
2 rl1
n
n
000
000 N-1
1 2 n N-1 N N+1
pl0
000
000
000
000
000
000
i
i
000
000
000
000
pl0
m0
rl1
m1
pl0
m0
rl1
m1
pl0
m0
rl1
m1
State Transition Diagram
Model Description
back
26. Performance Measures
Expected number of messages
Throughput
Average delay
0n
1,n1,n
1N
1n
0,n0,n
QPnQPn)N(E
1n
1,n1
1n
1,n0
QP mm
)N(E
D
back
27. 0
20
40
60
80
100
120
140
1.0 1.2 1.4 1.6 1.8 2.0
l0
E(N)
r=.3
r=.5
r=.7
0
10
20
30
40
50
60
1.0 1.2 1.4 1.6 1.8 2.0
l0
E(N)
p=.5
p=.7
p=.9
3
4
5
6
7
8
9
10
11
1.0 1.2 1.4 1.6 1.8 2.0
l1
E(N)
r=.3
r=.5
r=.7
2
3
4
5
6
7
8
9
10
11
1.0 1.2 1.4 1.6 1.8 2.0
l1
E(N)
p=.5
p=.7
p=.9
Fig. 2(a-b): Comparison of E(N) by varying l0 for different values of r and p respectively
Fig. 3(a-b): Comparison of E(N) by varying l1 for different values of r and p respectively
31. Concluding Remarks
The two service channels system are analysied by
considering switchover of messages from one channel to
another as can be realized in case of voice and data traffic
over ISDN network.
The model studied in this paper is capable to predict the
performance indices of real time system of
telecommunications wherein exclusive resources such as
transmission lines and switching nodes are shared by mixed
type traffic.
The further extension of work is possible by considering the
multi-class traffic which is the demand of multimedia services.
The computational tractability of the matrix geometric method
is validated by taking numerical illustrations.
Our study may be helpful to prevent congestion by routing
the traffic of excessive busy channel to less busy channel.
back
32. Section : 2C
In this section we consider, a Markovian retrial queue
with reneging
by considering the limited space in orbit.
The expressions for various performance measures
system state probabilities
number of customers in orbit
the expected waiting time in the queue
the expected waiting time in system
Numerical results are obtained to facilitate the comparison
of the corresponding model with infinite orbit capacity and
without reneging.
back
34. Assumptions
We consider a single server queue with Poisson input
rate l0 and l when server is idle and busy,
respectively.
The service time and retrial time are exponential
distributed with rate m and , respectively.
If server is busy then customer goes in the retrial
orbit of size N.
The customers in the orbit may retry for service with
probability r.
After joining the queue the customer may also renege
from the system.
The reneging is assumed to exponential distributed
with parameter .
back
35. Model Description
Let {St, Xt} denotes the system state space
where St is the number of customer being served at
time t
Xt represents the number of customers in the orbit at
time t.
The system is in state (i, j) if ith customer is being
served and j customers are in the orbit for i {0,1} and
j {0,1,2, …}.
The stationary probabilities
P0,j=P( S=0, X=j ), P1,j=P( S=1, X=j ),
back
36. The governing equations are constructed as
follows:
Solving above equations, we get
NjPjPj jj
0,)()( ,1,00
ml
10
,)1()1)(1()1( 1,01,11,1,00,1
Nj
PjPjrPrPPjrjr jjjjj
llml
1,1,00,1
)1(
NNN
PrPPjrN llm
NjP
j
j
P jj
0,
)(
,1
0
,0
l
m
back
38. Special Cases
1. Taking l0=l, we can derive the results for
homogeneous arrival rate.
2. Non-reneging behavior of customers has
obtained by putting =0
3. On substituting l0=l and =0, our results
coincide with the results of Elcan (1994) for
an M/M/1 queueing system with retrials.
39. Numerical Results
0
2
4
6
8
10
12
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
l0
E(K)
3
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
l
E(K)
3
0
2
4
6
8
10
12
14
16
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
E(K)
3
Figs. 1(a-c): E (K) by varying (a) l0 , (b) l and (c) g for different values of n
40. 0
2
4
6
8
10
12
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
l0
E(L)
3
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
l
E(L)
3
0
2
4
6
8
10
12
14
16
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
E(L)
3
Figs. 2(a-c): E (L) by varying (a) l0 , (b) l and (c) g for different values of n
41. 0
1
2
3
4
5
6
7
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
l0
E(K) r=.4
r=.6
r=.8
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
l
E(K)
r=.4
r=.6
r=.8
0.0
5.0
10.0
15.0
20.0
25.0
30.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
E(K)
r=.4
r=.6
r=.8
Figs. 3(a-c): E (K) by varying (a) l0 , (b) l and (c) g for different values of r
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
l0
E(L)
r=.4
r=.6
r=.8
0.0
2.0
4.0
6.0
8.0
10.0
1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
l
E(L)
r=.4
r=.6
r=.8
0.0
5.0
10.0
15.0
20.0
25.0
30.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
E(L)
r=.4
r=.6
r=.8
Figs. 4(a-c): E (K) by varying (a) l0 , (b) l and (c) g for different values of r
42. 0
2
4
6
8
10
12
14
0.1 0.3 0.5 0.7 0.9
E(K)
l0l
l0l3
l0l
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.1 0.3 0.5 0.7 0.9r
E(K)
l0l
l0l3
l0l
Figs. 5(a-b): E (K) by varying (a) g , (b) r for different set of l
0
2
4
6
8
10
12
14
0.1 0.3 0.5 0.7 0.9
E(L)
l0l
l0l3
l0l
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.1 0.3 0.5 0.7 0.9
r
E(L)
l0l
l0l3
l0l
Figs. 6(a-b): E (L) by varying (a) g, (b) r for different set of l
43. Conclusion
finite orbit M/M/1 retrial queue with reneging.
The incorporation of reneging parameter makes our
model more closer to real life situations.
The model, which presented here, has wide utility in
computer system, communication system and
manufacturing systems, etc.
44. Chapter : 3
we study the performance of Wireless Local
Loop (WLL) system in term of call loss and
delay probabilities.
We predict the performance measures for the
prioritized channel assignment scheme
The traffic in the system is considered as
integrated (voice/data).
We drive several analytical results based on
based on channel assignments
population size
back
47. loss system
wherein a new call to ST is lost if si=ci-chi
channels in the corresponding BS are busy
S=TC-R trunks in the BSC are occupied.
The handoff calls are lost if
all the reserved channels in the corresponding
BS are occupied
all the reserved trunks in the BSC are busy.
48. IPLM
Infinite traffic source i.e STs.
When there is heavy traffic on the network,
then it becomes desirable to serve the new
calls in a different manner.
This can be done by using modified scheme
where the new calls can use the r reserved
channels also in case of light traffic of
handover calls provided they are free
n
nf
2
1
)(
50. Loss probabilities of new calls at ith
(i=1,2,…,B) BS
Loss probability of handoff data and handoff
voice calls
ic
isn
ininewi
snfPL )(1,,
icicehandoffvoiiahandoffdati
PLL ,,,
51. At BSC
Loss probabilities new calls, handoff data and
handoff voice calls
TCnSP
n
Sjf
SnP
n
P
bscn
n
Sj
newbschandoffbsc
S
bsc
bscn
n
bsc
nbsc
1,
)(!
)(
0,
)(!
0,
1
,,
0,
,
hm
lll
hm
l
TC
Sn
nbscnewbsc
SnfPL )(1,,
TCbsccehandoffvoibscahandoffdatbsc
PLL ,,,
52. Delay Models
where handoff calls are allowed to wait in a
buffer of size Ni, if channels are not available.
we consider finite population model with
reserved channels and function.
It is assumed that the traffic at each BS is
originated from finite population of size K.
54. Conclusion
Reserving a fixed number of channels for handoff
calls reduces their call loss and delay
probabilities
The function scheme in IDMS and FDMM offers
least blocking to the handoff voice calls in
comparison new and handoff data.
Furthermore, releasing function used in channels
allocation for providing service to handoff voice
calls reduces blocking to the new calls also.
55. Chapter4
The double layer architecture is based
Macro layer :
The outer layer has connection to subscribers and
receives fresh traffic destinated for the network
Used to cover large areas with low traffic density
Radius: 1-20km
High-speed moving terminals (HSMT)
Micro layer :
The inner layer only deals with traffic inside the
network structure.
Used in areas with high traffic density
Radius:200m-2km
Low speed moving terminals (LSMT).
back
56. Hierarchical Cellular Architecture
Larger cells serve users with lower number of handoff but the
capacity is also reduced
Smaller cells lead to increase in capacity but also increase
handoffs
If a hierarchical architecture is used proper cell assignment can
lead to increased capacity
57. Analytical Models
Double Layer Cellular Model
New Call Bounding
Cuttoff Priority Scheme
Cuttoff Priority With Releasing Function
Double Layer Cellular Model With Subrating
New Call Bounding With Subrating
Cuttoff Priority With Subrating
Cuttoff Priority With Releasing Function and
Subrating
58. New Call Bounding Scheme
Threshold number for new
call bounding (NCB) scheme
is k
if the number of new calls in
the cell exceeds k, the new
call will be blocked.
Otherwise it will be admitted.
The handoff call is rejected
only when all channels in the
cell are used up.
59. )0,0(),(
!!
P
nm
P
n
h
m
n
nm
1
0 0
)0,0(
!!
k
m
mc
n
n
h
m
n
nm
P
0,,0 ncnmkm
Steady state probability in each micro cell
where,
k
m
mc
n
n
Lh
m
n
k
m
mc
Lh
m
n
Lh
nm
mcm
B
0 0
0
!!
)!(!
Blocking probability of handoff calls of LSMT
k
m
mc
n
n
Lh
m
n
kc
n
k
m
mc
Lh
m
n
n
Lh
k
n
n
nm
mcmnk
B
0 0
0
1
0
!!
)!(!!!
Blocking probability of new calls
60. For the macro cell, consisting N number of micro layers
i=1,2,…,C
0
!
.
q
i
N
q
i
Hh
i
For Macro cell
where
1
0
0
!
.
C
i
i
Hh
i
N
q
61. Handoff call of HSMT will be blocked in the macro cell if
Overall blocking probability
Carried Load
!
.
C
N
B
C
Hh
Hh
l
lll HhHhLhLhnn BBB
B
l
lll )1()1()1( HhHhLhLhnn BBB
CL
62. Cutoff Priority Scheme (COP1)
Threshold number for cutoff
priority scheme is s
if the total number of busy
channels exceeds a threshold
limit s, and a new call arrives,
the new call will be blocked
otherwise it will be admitted.
The handoff call is rejected only
when all channels in the cell are
used up.
if we assume that there is no
new call bounding but r=c-s
channels are reserved to give
priority to handoff traffic in micro
cell.
63. Steady state probability
cis,P
!i
si,P
!iP si
Lh
s
Lhn
i
Lhn
i
1
0
0
0
s
i
c
si
si
Lh
s
Lhn
i
Lhn
c
si
si
Lh
s
Lhn
n
!i!i
!iB
0 1
1
s
i
c
si
si
Lh
s
Lhn
i
Lhn
sc
Lh
s
Lhn
Lh
ii
cB
0 1 !!
!
64. Cutoff Priority Scheme With Releasing
Function (COPF1 )
Steady state probability
cisP
i
sif
siP
iP si
n
s
Lhn
i
Lhn
i
Lh
1,
!
)(
0,
!
0
0
…13
P0 is also obtained by using normalization condition i.e. .
1
1
0
c
i
P
65. Blocking probability of new calls
s
i
c
si
si
n
s
Lhn
i
Lhn
c
si
si
n
s
Lhn
n
i
sif
i
i
sif
B
Lh
Lh
0 1
1
!
)(
!
!
)(
…14
Blocking probability of handoff calls
s
i
c
si
si
n
s
Lhn
i
Lhn
si
n
s
Lhn
Lh
i
sif
i
c
sif
B
Lh
Lh
0 1
!
)(
!
!
)(
…15
Numerical Result
66. Optimal Channel Allocation
Our aim in this section is to decide the number of
channels to be allocated in each cell so that the
blocking probabilities of the new and hand-off calls
could be minimized.
Three channel assignment schemes are concerned
to reduce the blocking probability of handoff attempts.
The channel assignment schemes are based on new
call bounding and cutoff priority criteria.
back
67. we propose an algorithm to assign optimal number of
unreserved channels (si) and guard channels (ri) to
each cell in the cluster;
we have total C channels in a cluster of a cellular
radio network depending on the blocking probability
of the new and handoff calls.
By applying the proposed algorithm to each cluster in
the overlaid area, the prioritized channel assignment
for double layer cellular network is proposed for the
whole network.
68. We want to find the smallest integer such
that
We formulate a Non-Linear Integer
Programming Problem (NIPP) for calculating
the optimal number of channels in each cell
which minimize the overall handoff call
blocking probability as:
0
js
max
0
,
)0,( BsB jnj
72. Chapter 5
Cellular Radio System Supporting Integrated Traffic
Call Admission Control And Blocking Probability
Estimation In PCS Networks
Handoff Prioritization Scheme And Optimal Allocation
back
73. Soft Computing
Provides flexile Information processing
capabilities for real life situations
Has the properties of approximation ad
dispositionality
74. Artificial Neural Network
We can simulate the structure of human brain
it is closely modeled on biological process
Such a structure is formed as “Artificial
Neural Network” structure.
We can train a neural network to perform a
particular function by adjusting the values of
the connection between elements.
76. Performance measures
The blocking probability of new and handoff
calls are calculated as
The overall blocking and carried load are
cknmrc
n knmpB ),,(
cknm
h knmpB ),,(
hhnn
BB
B
l
)1()1( hhnn
BB
CL
l
79. Section 5B
two-dimensional traffic is studied
by using Neuro-fuzzy approach
which readjusts the connection weight, by using
Backpropogation algorithm and simplifies the fuzzy rule
proposed prioritized scheme
by subrating an occupied channel on a blocked port
can be spilitted into two half rated channels to
accommodate more voice handoff attempts.
83. Section 5C
we consider the channel assignment scheme for two
traffic classes, voice and data by incorporating
balking
reneging
two-threshold guard channel policies.
channel allocation scheme
There is a provision of buffer for class-2 handoff.
In the proposed call admission scheme, the total
number of channels is sub divided into three subsets
ordinary channel, shared channels and dedicated guard
channels
84. 0 1
l
mh
C-k C
l h2
(C-k+1)(mh
C-
r-k
ll
2mh
C+
N
(C-r-k)(mh
00
0
l
h
(C-r-k+1)
(mh
l
h
(C-k)
(mh
l
h2b
l
h2b
C(mhNC
(mh
00
0
00
0
l h2
C(mh
00
0
NCnCP
ClCC
CnrCP
n
rCnkrCP
n
krCnP
n
P
n
Cj
Cn
h
r
h
k
h
krC
rCn
h
k
h
krC
krCn
h
krC
n
n
,
)()(!
,
!
,
!
0,
!
0
1
22
0
)(
2
0
)(
0
hm
bl
87. if
2h2h P)C,C,C(B
then
return (C,C)
end if
set
Cr
while
)P)r,k,C(B( 2h2h
do
set
1rr
end while
set
rk
while
)P)r,k,C(B( 1hh
do
if
)P)1r,k,C(B( 2h2h
then
set
1rr
else
set
1kk
end if
end while
return (k,r)
88. Properties of Blocking Probabilities
Property for blocking probability of new calls
Blocking probability of Bn decreases by increasing the number
of ordinary channels (C).
Blocking probability of new calls increases by increasing the
number of shared guard channels (k).
Blocking probability of new calls increases by increasing the
number of dedicated guard channels (r).
Property for blocking probability of handoff calls
Bh decreases as the number of ordinary channels (C)
increases.
Blocking probability of handoff calls decreases as the number of
shared guard channels (k) increases.
Blocking probability of handoff calls increases as the number of
dedicated guard channels (r) increases.
89. (c) Property for blocking probability of handoff
calls of class-2 traffic
Blocking probability of Bh2 decreases with the
increase in the number of ordinary channels (C).
Blocking probabilities of Bh2 decreases with the
increase in the number of shared guard channels (k).
Blocking probabilities of Bh2 decreases by increasing
the number of dedicated guard channels (r).
90. Conclusion
The incorporation of balking and reneging behavior
improves the model as it deals with more realistic
situations.
An optimal allocation algorithm is developed which
can be easily implemented as validated by
performing extensive numerical experiment.
The inclusion of two threshold levels in place of one
threshold level for allocation of guard channels
makes our model more versatile to deal with real time
wireless communication systems.
92. About VoD…
A video server stores compressed video for delivery (MPEG
Compressed format) to clients connected by a network.
The request of a particular file depends on the popularity or
demand.
Video data are distributed among the streams and multiple
streams
where the user can watch any movie at any time
he can pause / resume the video or rewind/forward it as per
his convenience
The request categorized into two types
Prioritized
Non-prioritized
VoD is based on:
programming
client-server architecture
93. Mathematical Modeling
IUM
The number of users are
assumed to be infinite
FUM
The number of users are
taken as finite
We Investigate analytical models
Models are compared on the basis of the blocking probabilities
We consider two models for analyzing the performance of the
video server
94. Infinite User Model (IUM)
The steady state probabilities of being ith streams in the disk is
…1
where
…2
!
!
)(
i
i
P
i
j
RIi
r
Ri
i
j
i
jjjj
a
a
jjj
jj
IiRI
RIi
10
1)(1
0
0
!!
ii
P
i
j
RIi
r
RiIj
RjIji
i
j
RjIj
i
jjjj
aa
96. The overall blocking probability of the jth disk
pn
p
jp
n
jn
ρρ
ΒρΒρ
jB
The overall blocking probability of video server
D
1j
jjBαB
The overall blocking probability of the jth disk
pn
p
jp
n
jn
ρρ
ΒρΒρ
jB
The overall blocking probability of video server
D
1j
jjBαB
…5
…6
…7
…8
97. Finite Users Model (FUM)
Ij
RjIji
i
j
RjIji
r
RjIj
RjIj
i
i
j
Ij
RjIji
i
j
n
j
i
M
i
M
ii
M
B
aa
a
)(
1
0
()(
!
)(
Ij
RjIji
i
j
RjIji
r
RjIjRjIj
i
i
j
Ij
j
Rj
r
RjIj
p
j
ii
M
ii
M
i
M
B
!
(
!
)( )(1
0
aa
a
…10
…9
Blocking probabilities of the prioritized
Blocking probabilities of non – prioritized
98. OPTIMAL TRAFFIC ALLOCATION
Here we suggest an iterative algorithm for determining the optimal
portion of the traffic to be allocated on the disks of the video
server, such that the overall blocking probability could be
minimized.
Overall blocking probability
Where
For minimizing
is the blocking probability of disk j at
D
j
jj BB
1
a
D
j
j
1
1a
D
j
jKBL
1
1a
opt
jj
j
opt
j BB aa
)(
opt
jB opt
ja
99. Fig.1 (a): File allocation for disk in the VoD
server
D=15, Ij=20; 1<=j<=4 ,Ij=15;
5<=j<=15
Two type of disks
Disks 1-4 have 20 I/O streams, qi =80%, for j=1-4
Disk 5-15 have 15 I/O streams, qj =6% for j=5-15.
Two different disks having equal I/O streams get equal percentage of traffic.
8%
8%
9%
9%
6%
6%6%6%
6%
6%
6%
6%
6%
6% 6%
File allocation for disk in the VoD server
D=15, Ij=20; 1<=j<=4 ,Ij=15; 5<=j<=15
100. Blocking probabilities of the
individual disk for prioritized
users request
Blocking probability of disk
and server
1.E-33
1.E-31
1.E-29
1.E-27
1.E-25
1.E-23
1.E-21
1.E-19
1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
l
blockingprobability
bp 1-4
bp 5-15
Fig. 2(b): Blocking of individual disks for
prioritized traffic in IUM
1.E-24
1.E-22
1.E-20
1.E-18
1.E-16
1.E-14
1.E-12
1.E-10
1.E-08
1.E-06
1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
l
blockingprobability
bq 1-4
bq 5-15
B
Fig. 2(c): Overall blocking of disks and
server for IUM
101. Fig 3(a): Blocking of individual disks for non -
prioritized traffic in FUM
1.E-21
1.E-19
1.E-17
1.E-15
1.E-13
1.E-11
1.E-09
1.E-07
0.2 0.4 0.6 0.8 1
l
blockingprobability
bu 1-4
bu 5-15
Fig. 3(b): Blocking of individual disks for
prioritized traffic in FUM
1.E-40
1.E-37
1.E-34
1.E-31
1.E-28
1.E-25
1.E-22
1.E-19
1.E-16
0.2 0.4 0.6 0.8 1
l
blockingprobability
br 1-4
br 5-15
1.E-22
1.E-20
1.E-18
1.E-16
1.E-14
1.E-12
1.E-10
1.E-08
1.E-06
0.2 0.4 0.6 0.8 1l
blockingprobability
bq 1-4
bq 5-15
B
a
Fig. 3(c): Overall blocking of disks and
server for FUM
Blocking probabilities of the
individual disk for prioritized
Users.
Blocking probability of the
disk for non-prioritized
users.
Overall blocking probabilities of
the disk and the video server
102. Fig. 4(a): Blocking of individual disks for non -
prioritized traffic in IUM
1.E-32
1.E-28
1.E-24
1.E-20
1.E-16
1.E-12
1.E-08
1.E-04
2 4 6 8 10 12
r
blockingprobability
bn 1-4
bn 5-15
Fig. 4(b): Blocking of individual disks for
prioritized traffic in IUM
1.E-22
1.E-21
1.E-20
1.E-19
1.E-18
1.E-17
1.E-16
2 4 6 8 10 12
r
blockingprobability
bp 1-4
bp 5-15
Fig. 4(c): Overall blocking of disks and server
for IUM
1.E-20
1.E-18
1.E-16
1.E-14
1.E-12
1.E-10
1.E-08
1.E-06
1.E-04
1.E-02
2 4 6 8 10 12
r
blockingprobability
dq 1-4
dq 5-15
B
Both the models, the increase in
the number of reserved channels
result in low blocking of prioritized
user request.
Blocking of non- prioritized user
increases with the increase in
reserved channels.
Optimal value of r
103. 1.E-08
1.E-07
1.E-06
1.E-05
1.E-04
1.E-03
2 3 4 5 6 7 8 9 10
r
blockingprobability
br 1-4
br 5-15
Fig. 5(a): Blocking of individual disks for
non prioritized traffic in FUM
Fig. 5(b): Blocking of individual disks for
prioritized traffic in FUMs
1.E-37
1.E-34
1.E-31
1.E-28
1.E-25
1.E-22
1.E-19
1.E-16
2 3 4 5 6 7 8 9 10
r
blockingprobability
bu 1-4
bu 5-15
1.E-09
1.E-08
1.E-07
1.E-06
1.E-05
1.E-04
1.E-03
2 3 4 5 6 7 8 9 10
r
blockingprobability
bq 1-4
bq 5-15
B
Fig. 5(c): Overall blocking of disks and
server for FUM
Blocking of individual disks
non prioritized traffic in FUM
Increase in the number of reserved
channels result in low blocking of
prioritized user request
Optimal value of r
104. Blocking probabilities for
FUM are shown by varying N.
More number of subscribers
(users) lead to higher B. P.
of the disk as well as of the
server.
Fig. 6(a): Blocking of reserve streams by
varying N for FUM
1.E-10
1.E-09
1.E-08
1.E-07
1.E-06
30 31 32 33 34 35 36 37N
blockingprobability
br 1-4
br 5-15
1.E-10
1.E-09
1.E-08
1.E-07
1.E-06
30 31 32 33 34 35 36N
blockingprobability
bq 1-4
q 5-15
b
Fig 6(b): Overall Blocking disks and server of
reserve streams by varying N for FUM
105. Conclusion
The performance of a VoD.
Minimizing the overall blocking probability.
Reserving fixed number of streams for the prioritized users can
considerably minimize there blocking probability.
Optimal value of the number of reserved channels.
The optimal load sharing policy
Optimal Allocation
The total capacity usage
107. Section 1
use of client buffering
to reduce bandwidth requirement
for Video broadcasting over broadband network
which needs a high number of streams for low
user delay
schemes
(JAS), in which a movie is broadcasted in
staggered manner and short unicast streams
are used.
(SBB), which provides a broadcasting
strategy for popular movies.
108. Aim
Our aim in this section is two folds:
(i) analyse and optimize JAS and SSB schemes,
which is appropriate for movies of intermediate
request rate and popular movies
(ii) to achieve minimum server bandwidth.
Traffic
hot
old movies
109. Model Description
the request of a particular file depends on the
popularity of that file.
Video data are distributed
uni streams
multiple streams
which work together to serve Video-streaming
requests from other streams.
110. movie of length Lmin at every Dmax minutes to satisfy the
delay goal.
In scheme-1, the Video
is multicasted in a
staggered manner at
regular offset point of
Ts minutes.
If a request arrives less
than Dmax minutes
before the start of
multicast, it waits till the
start of multicast and
join the stream
otherwise it is served
immediately.
In scheme-2, the server
streams are grouped
into multicast channels.
111. The number of multicast streams is L/Ts.
The distribution f (x) of inter-arrival time is
the number of concurrent unicast stream is
x
exf l
l
)(
dx
T
xxf
N
DsT
s
max
0
)(
112. Buffering Schemes
Join-and-Stream (JAS)
the total number of streams is the sum of the
multicast and unicast streams.
optimal batching
dx
T
ex
N
DsT
s
x
u
max
0
1
l
l
s
m
T
L
N 1
max
DTB s
113. Stream-Bundling Broadcasting (SBB)
For increasing bandwidth with an increment of C
bit/min the server streams are bundled together
so that more customers can serve quickly
the number of streams required is the sum of the
broadcast/multicast streams and the stream in the
bundled channel.
The average bundled streams used is
the total number of streams required
2
1
2
/
max
max
1
max1
s
s
DsT
i
su
D
T
TiDN
2
1
2 max
s
s
s
D
T
T
L
N
115. Section 2
we study the use of client buffering to reduce such
bandwidth requirement.
In a proposed framework, a video will be delivered to
customer through one of two channels,
unicast and multicast,
with Neighbors buffering based Video-on-Demand
(NBB-VoD) architecture.
The two schemes are developed
to select appropriate delivery channel
An adaptive batching scheme
is suggested in which the optimal batching time
is calculated on the basis of arrival rate.
116. Batching Schemes
unicast bandwidth 2 Cbit bandwidth
for multicast transmission customers need Cbit
bandwidth.
batching time
In this policy time is divided into an interval of Wmin
each interval multicast stream is opened.
They will be group together and served by one
multicast group
then bandwidth requirement B for one multicast group
is
))(2()2)((...)2)(2()2(1 bithbitbitbitu
CxLCxCxCxB b
the customer can join the multicast group only when first unicast stream is released
because the multicast stream is started when the first customer joins it.
117. The probability density function f (x)
Unified VoD
In this case, i = 2 and a = 2.
n
i
i
ixii
ii
xexxf 1
1
0,1,)( aal
ala
min
1min
0
1
1
.
)(.
WC
dxxfB
N
bit
W
u
u
1min
0
)())(2()1)((
w
bithbit
dxxfCxLxC bb
118. Multicast Transmission
Multicast streams start transmitting data after
the arrival of first request
the requests with a waiting time x will have
their desired data from upcoming multicast
channel, multicast bandwidth during one slot
of time is given by:
minmin,
min,
1
1
10)(
WxWCL
WxCxL
B
bith
bith
m
min
1min
1min
0
1
)()()(
w
W
bith
w
bithm
dxxfCLdxxfCxLN
119. NBB-VoD
the highest buffering occurs when the user
arrives slightly before the next multicast to
hold one slot time.
For the first request we assign unicast
channel to transmit x time unit for request.
Then the remainder of the video data will be
transmitted via a multicast channel.
120. Unicast Transmission
Thus the required unicast bandwidth is given
the average number of unicast bandwidth
requirement in NBB-VoD
1
0,)1(
min,
2
WxkxC
kxxC
B
bit
bit
u
1min
0
22
)(.
W
uu
dxxfBN
k W
k
bitbit
dxxfxCdxxfxC
0
min
)()(
2
)1(
121. Multicast Transmission
The multicast bandwidth requirements in
case of NBB-VoD are identical with those of
Unified-VoD case
123. Contribution to knowledge
Optimize or fine-tune the telecommunication
infrastructure to increase capacity flexibility
performance without necessarily increasing
cost.
CCN studied will deal with in a wide variety of
applications like remote login, distributed
database systems, parallel computing,
distance education and manufacturing
control etc.
To diagnose and improve the situations that
are creating delay and blocking.