Thesis : "IBBET : In Band Bandwidth Estimation for LAN"

IBBET : In Band Bandwidth Estimation for LAN

Vishalkumar Soni
Thesis Advisor : Dr. Yusuf Ozturk
MS Electrical & Computer Engineering
Department of Electrical & Computer Engineering
Computer Networks Research Lab
Spring 2012

Outline
 Need for Bandwidth Estimation
 Bandwidth Metrics
 Network Pipe Model
 Types of Bandwidth Estimation
 Packet Dispersion
 In Band Bandwidth Estimation
 Hypothesis : IBBET
 Pearson Correlation Coefficient
 Regression Analysis
 Matlab Implementation & Results
 IBBET Implementation
 Flowchart of Server
 Flowchart of Client
 Test Bed Setup
 IBBET Test Results
 Conclusion

Need for Bandwidth Estimation

 Rate based Streaming Application
 Verification of Quality of Service (QOS)
 Routing of packets
 Admission Control
 Resource Management

Bandwidth Metrics
 Bandwidth : The maximum number of bits a link can transfer per
unit of time [6].
 Consider a network between two computer separated by n hops
 Narrow Link : Link with minimum capacity sets upper bound for
capacity of whole path
Cn = min{Ci} (1)
i = 1,2.....n

 Available Bandwidth : The unused capacity of link for given
interval of time
ABw(t ) = Cn − Rx (t ) (2)

: Available Bandwidth
ABw(t )
Cn : Link with minimum capacity along the path
Rx(t ) : Measured Cross traffic
 Tight Link : The link with minimum available bandwidth along the
network path

Network Pipe Model
 Consider LAN network as pipe model [6].

 Processing Delay : The time to process packet through protocol
stack
 Latency : The time spend by packet during transmission in a link
 Queuing Delay : The time spent by packet in FIFO buffer of
router due to cross traffic
D = ∑ ( P + L + Q) (3)
D: Total delay experienced by the packet route from sender to receiver
P: Processing delay experienced by packet
L: Latency delay experienced by packet
Q: Queuing delay experienced by packet

Types of Bandwidth Estimation
 Active Probing
 Probe packets are sent to estimate bandwidth
 Principle : If transmission rate of packets exceeds available
bandwidth then it increases queuing delay and reduce reception
rate
e.g. Train of Packet Pair [6], PathChirp [3]

 Passive Probing
 Application packets are monitor to estimate bandwidth
 Principle : It is based on round trip time of acknowledgement
packet for corresponding TCP packet sent
e.g. Passive Access Capacity Estimation [4], Idle Gap [8]

Packet Dispersion
 Packet Dispersion technique over three link model [1].

 Capacity of link : C and Packet Size : L
 Consider two packets are send back-to-back as shown above
 Transmission delay : ∆ = L C
 Receiver measures capacity of link as : C = L ∆

In Band Bandwidth Estimation

 Major Classification of Bandwidth Estimation

 Out of band
 In band

 Cons for using out of band bandwidth estimation technique

 Congestion
 Latency
 Degrades overall utilization of channel
 Degrades QOS for real time application such as audio or video

Hyothesis : IBBET
 Reshaping application packets such as audio or video to infer
bandwidth
 Principle : Packet Dispersion
 Traffic shaper at server

◦ Reshapes stream of application packets
◦ Parameters : Packet size, Inter departure time
 Characteristics of traffic shaper

◦ Reshape application packets in a network friendly manner
◦ Minimum number of application packets in train
 Transmitted signature pattern is distorted due to bandwidth
limitation of network
 The receiver needs to detect signature of transmitted pattern from
distorted pattern using one of auto correlation function
 Regression analysis is performed to infer bandwidth

Pearson Correlation Coefficient
 It is defined as covariance of two variables divided by product of
standard deviation [10].
cov( X , Y )
ρx , y = (4)
σxσy
 For paired data ( Xi , Yi ) of n data samples, the sample Pearson
correlation coefficient is
r=
1 n
∑
n − 1 i =1
[( X σ X )(Y σY )]
i−

x
m i−

y
m
(5)
Xm Ym : Sample mean
σ x σy : Sample standard deviation

Correlation Coefficient Interpretation Negative Positive
None -0.09 to 0.0 0.09 to 0.0
Small -0.3 to -0.1 0.3 to 0.1
Medium -0.5 to -0.3 0.3 to 0.5
Large -1.0 to -0.5 0.5 to 1.0

Regression Analysis
 It is used for modeling relationship between dependent variable and
one or more independent variables [10].
 The mathematical regression model can be represented as follows

Y ≈ f (X ,β) (6)
Y
X
: Dependent variable
β
: Independent variable
: Unknown parameters ˆ ˆ
yi = β0 + β1 xi
 A linear regression model is represented as

(7)
ˆ ∑ ( xi − x )( yi − y )
 For linear regression, the unknown parameters x
β1 =
ˆ
β 0 = y − β1 (8)
are computed as
∑
( xi − x )( xi − x )
x x
y y
: Mean of values

Matlab Implementation

Algorithm
2. Specify configuration of signature pattern and network constrain.
3. Generate signature pattern for defined number of iterations.
4. The signature pattern is distorted based on user emulation of
bottleneck link capacity and network distortion.
5. At receiver, transmitted signature pattern is recognized from
distorted signature pattern using pearson correlation coefficient.
6. Linear regression analysis is applied to fit line between reception
rate and time stamps of distorted signature pattern packets.
7. It estimates the slope and intercept for six consecutive packets.
8. The algorithm estimates bandwidth per pattern as average of
reception rate of packets with slope less than threshold
9. The bandwidth for pattern stream is calculated as average of
bandwidth per pattern.


The following are condition applied to signature pattern and network

 Enter maximum rate of the transmitted probe signal in Mbps: 15
 Enter constant rate of the stream in Mbps: 3
 Enter number of packets for constant rate of stream: 12
 Enter number of iteration of cycle: 4
 Enter bottleneck link capacity: 9
 Enter amount of network distortion in percentage: 6
 Enter size of the packet in bytes: 1024

 The estimated bandwidth at receiver is 8.8083 Mbps


Transmitted Signature Pattern


Received Signature Pattern


Pattern detection

IBBET Implementation
 Signature Pattern Equations

y(t) = α f (t ) × T (9)
Where f ( t ) = f ( t − 1) + n
Where,
y(t) = α f (t ) × T (10) y(t ) : Inter departure time of packets
Where f ( t ) = f ( t − 1) + δ ( t )
α : Constant exponent coefficient
T : Initial constant inter departure
y(t) = α f (t ) × T (t) (11) time of packets
T (t ) : Time varying initial inter
Where f (t ) = f (t − 1) + δ (t )
departure time of packets
n : Constant increment

δ (t ) : User defined time varying
increment

IBBET Implementation
Search Window

0 Mbps 10 25 70 90 100 Mbps
Bandwidth Range

Signature Pattern
Shape

Flow Chart of Server
Define Destination Configure Probing Create UDP
Start Sock == -1
Port & IP Address Pattern Socket

Exit

NO

Memory Allocation Assign Destination IP &
Initialization of
To Port to Server Address
Packet
Buffer Structure

Current Iteration
NO End
<= DefineCycles

YES

CurrentPacket <=
NO
Defined Packets of
Probe Train

YES
Send Packets for
Send Packet Constant Stream

Sleep for Constant Period YES
Estimate Inter Departure Time

Log Transmission Rate,Inter Departure CurrentPacket <=
Time & Packet Sequence
Defined Constant
Stream Packets

Change Sleep period based
on probing equation

NO

Flow Chart of Client
A
Receive Packet
Start

Estimate Inter Arrival Time

Define Port Estimate Reception Rate
per Packet
NO

Logging of Reception Rate per packet, inter arrival time, Packet
Sequence
Create UDP
Socket

End of Probing
Stream

YES
Sock == -1 YES Exit

Read expected inter arrival time of probe stream

NO
CurrentPattern
NO
<= Defined Pattern
Assign Port & IP
Address to Client
Address Structure YES
Bw = Avg. Pattern Bw over No. of
Received Pattern
Measure difference between expected &
actual inter arrival time of Packets

End

Bind Socket to
Listening Port
Error <
NO
Thresold

YES

Store Supported Receive Rate

bind == -1 YES Exit

End of
NO Pattern Packets

NO YES

A
Pattern Bw = Last Supported
Received Rate *Scale Factor

Test Bed Setup
 Server : Streams signature pattern
 WANem : Puts bandwidth constrain on interface as per configuration
 Client : Estimates the bandwidth

Router

Physical Connection

Flow of Packets

WANem
Client Server
Emulator PC

IBBET Results
Signature Pattern from equation (9)
y (t ) = α f (t ) × T Where f ( t ) = f ( t − 1) + n

Configuration of Signature Pattern & WANem
 Number of packets in signature pattern : 40
 Incremental : 0.2
 Alpha coefficient : 0.7
 Packet size : 1024 B
 Probing range : 1 to 18 Mbps
 Constant rate stream : 3 Mbps
 Number of constant rate stream packets : 15
 WANem bandwidth constrain : 4 Mbps
 Number of iterations : 5

IBBET Results

Inter departure time between packets at server for equation (9)

IBBET Results

Inter arrival time between packets at client for equation (9)

IBBET Results

Bandwidth (Mbps) Pattern
Sequence
3.368421 1
4.535991 56
3.521926 111
4.818823 166
4.108325 221
Bandwidth estimation for equation (9)

Avg. Bandwidth = 3.368421 + 4.535991+ 3.521926+4.818823+ 4.108325
5

Avg. Bandwidth = 4.070697 Mbps

IBBET Results

y(t) = α f (t ) × T Where f ( t ) = f ( t − 1) + n

 Number of packets in Signature Pattern : 40
 Incremental : 0.2
 Alpha Coefficient : 0.7
 Packet Size : 1024 B
 Probing Range : 1 to 18 Mbps
 Constant Rate Stream : 3 Mbps
 Number of Constant Rate Stream packets : 15
 WANem bandwidth Constrain : 5 Mbps
 Number of Iterations : 15
 The standard deviation is computed based on following equation
N
~
∑ ( Xi − X ) 2

SD = i =1

N −1

IBBET Results


IBBET Results

Standard deviation for equation (9)

IBBET Results

y(t) = α f (t ) × T Where f ( t ) = f ( t − 1) + δ ( t )

 Incremental value for first 30 Packet : 0.2
 Incremental value for remaining 10 Packet : 0.4

IBBET Results


IBBET Results

Bandwidth (Mbps) Pattern
Sequence
6.989761 1
4.452174 56
6.400000 111
4.830189 166
3.416180 221


The average bandwidth over the five iteration is 5.217660 Mbps

IBBET Results
y(t) = α f (t ) × T Where f ( t ) = f ( t − 1) + δ ( t )

 Incremental value for first 30 Packet : 0.2
 Incremental value for remaining 10 Packet : 0.4
N
~
∑ ( Xi − X ) 2

SD = i =1

N −1

IBBET Results


IBBET Results
y (t ) = α f (t ) × T (t ) Where f (t ) = f (t − 1) + δ (t )
 Initial Probing Rate : 4 Mbps
 Time varying Incremental value for Packets : 0.1

IBBET Results


IBBET Results

Pattern
Bandwidth (Mbps)
Sequence
5.535135 1
6.291859 56
6.671010 111
6.872483 166
6.095238 221


The average bandwidth over the five iteration is 6.293145 Mbps

IBBET Results
y (t ) = α f (t ) × T (t ) Where f (t ) = f (t − 1) + δ (t )
 Initial Probing Rate : 3 Mbps
 Time varying Incremental value for Packets : 0.1
N
~
∑ ( Xi − X ) 2

SD = i =1

N −1

IBBET Results


Conclusion

 On comparing equations (9), (10) and (11) for WANem bandwidth
constrain of 5 Mbps. Equation (11) has least standard deviation
over period of 15 patterns.
 Hence, we can say that equation (11) will require minimum number
of probes to get fairly accurate bandwidth estimation.
 Moreover, equation (11) provides more parameters to change in
order to adapt signature pattern to dynamically changing
bandwidth.
 Our Matlab simulation and WANem test results revels that IBBET
estimate bandwidth fairly accurate.
 This algorithm will reduces congestion on network and reduces
time for rate adaptation at server.

References
[1] Constantious Dovrolis, Parameswaram Ramanathan and David More,“What do a packet dispersion technique
measure?”, INFOCOM, Twentieth Annual Joint Conference of the IEEE Computer and Communications
Societies, IEEE, p.905 - 914 vol.2,2001.
[2] Bob Melander, Mats Bjorkman and Per Gunningberg, “A New End-to-End Probing and Analysis method for
Estimating Bandwidth Bottlenecks”,Proc.IEEE, GLOBECOM, 2000.
[3] Vinay J. Ribeiro, Rudolf H. Riedi, Richard G. Baraniuk, Jiri Navratil, and Les Cottrell, “pathChirp: Efficient
available bandwidth estimation for network paths”, In Passive and Active Measurment Workshop, April 2003.
[4] Fornasa, Martino, Maresca and Massimo, “Passive Access Capacity Estimation” ,International Journal of
Network Management, NEM-09-0070, University of Padova, 2009.
[5] Ningning Hu and Peter Steenkiste, “Estimating Available Bandwidth Using Packet Pair Probing”, School of
Computer Science, Carnegie Mellon University, Pittsburg, PA, 2002.
[6] Dimas Lopez Villa and Carlos Ubeda Castellanos, “Study of Available Bandwidth Estimation Techniques to be
applied in Packet-Switched Mobile Networks”, Department of Communication Technology, Aalborg University,
2006.
[7] Abhik Majumdar, Daniel Grobe Sachs, Igor V. Kozintsev, Kannan Ramchandra, and Minerva M. Yeung,
“Multicast and Unicast Real-Time Video Streaming Over Wireless LANs”, IEEE Transactions on Circuits and
Sysems for Video Technology, Vol. 12, No. 6, June 2002.
[8] Heung Ki Lee, Varrian Hall, Ki Hwan Yum, Kyoung Ill Kim and Eun Jung Kim, “Bandwidth Estimation in
Wireless LAN for multimedia streaming services”, Texas A & M University, University of Texas San Antanio,
Electronics & Telecommunication Research Institute.
[9] Mingzhe Li, Mark Claypool and Robert Kinicki, “WBest : A Bandwidth Estimation tool for Multimedia
Streaming Application for IEEE 802.11 Wireless Networks”, Computer Science Department at Worcester
Polytechnic Institute, Worchester, MA, USA.
[10] Walter A. Rosenkrantz “Introduction to probability and statistics for scientist and engineers” McGraw Hill
Series In, ISBN 0-07-053988-X.

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