ESTIMATION OF THE DISTRIBUTIONS OF THE QOS

PARAMETERS USING SAMPLED PASSIVE MEASUREMENTS

TECHNIQUES

Yazeed A. Al-Sbou

a,b

Reza Saatchi

Samir Al-Khayatt

Rebecca Strachan

Sheffield Hallam University, Faculty of Art, Computing, Engineering and Science, City campus, Howard Street,

Sheffield, S1 1WB, UK

Keywords: Quality of Service (QoS), network measurements, traffic-sampling methods, network performance analysis.

Abstract: As networks grow in complexity and scale, the importance of network performance monitoring and

measurement also increases significantly. High data rates often lead to large amount of measurement results.

Therefore, in order to prevent an exhaustion of the network resources and to reduce the measurement cost, a

reduction of the collected data is required. A performance measurement method for estimating the actual

network performance, experienced by the user, has been proposed. This study focuses on monitoring the

network performance and estimates its main Quality of Service (QoS) parameters (delay, throughput, and

jitter) through the use of a non-intrusive passive measurement method based on sampling methodologies.

This method will overcome the drawbacks of both active and passive monitoring methods. That is because it

measures the actual performance experienced by the user and requires reduced calculations of QoS

parameters from the sampled packets. The validation of this approach was analysed and verified through

simulations. Three different sampling techniques (systematic, random, and stratified) were investigated. The

study indicated that an accurate estimation of the QoS parameters could be obtained without the need to

measure across the whole packets of traffic information. As a result, the scheme has shown an estimation of

the detailed characteristics of performance for each user. For a bottleneck based network topology and

traffic conditions used, the random sampling showed the best overall performance.

1 INTRODUCTION

Quality of Service (QoS) network measurement and

analysis have long been of interest to the networking

research community. The analysis of network QoS is

based on measuring the network dynamic

parameters to provide some insight into the way the

user traffic is treated within the network. Monitoring

and measurement schemes usually fall into two

categories: passive and active methods. A passive

measurement is based on achieving measurement of

the actual traffic load in the network. This category

often needs the storage and processing of very large

amount of data. An active measurement, on the other

hand, is based on generating (probing) a new traffic

to be used to get the measurements statistics. In this

case, the QoS and performance of the probe-packet

stream, which is sent periodically, is monitored to

determine (infer) the QoS and the performance of

the user's packets and the network directly. Many

active monitoring tools have been developed to

monitor the network performance (CAIDA, 2005).

When using an active method, the probe packets

will perturb the network. In addition to that,

sometimes the measurements of the probing packets

do not represent the actual user measurements (Aida

et al., 2002). Passive measurements have the

advantage of not adding an extra load to the

network. However, they require the transfer of the

captured data for comparison with the other data and

the identification of each packet by its header or

content, which is hard when the data volume is huge.

Therefore, passive measurements have the

disadvantage of requiring substantial resources for

comparison and computation (Ishibashi et al., 2004).

A combination of both active and passive

methods could be employed for performance

measurement. A performance measurement method,

Change-of-Measure based Passive/Active

Monitoring (CoMPACT Monitor), was used for

324

A. Al-Sbou Y., Saatchi R., Al-Khayatta S. and Strachan R. (2005).

ESTIMATION OF THE DISTRIBUTIONS OF THE QOS PARAMETERS USING SAMPLED PASSIVE MEASUREMENTS TECHNIQUES.

In Proceedings of the Second International Conference on e-Business and Telecommunication Networks, pages 324-329

DOI: 10.5220/0001410503240329

 SciTePress

estimating the actual network performance

experienced by users (Aida et al., 2002 and Ishibashi

et al., 2004).

In order to overcome some of the disadvantages

of both active and passive schemes, sampling

methodologies can be employed. Using these

methodologies for the passive method will reduce

the amount of data to be processed, reduce the

demand on the overhead processing time of the

collected data, and hence speed up the performance

measurement results. In addition, there is no need

for artificial traffic to be injected which will perturb

the network and bias the measurements as in the

active method.

Sometimes, the estimation of the network or user

performance may be difficult to be obtained from

direct measurements of the whole traffic. In this

paper, a scalable and efficient measurement

approach has been used to estimate the network

performance experienced by users and it has been

used to estimate the dynamic QoS parameters

(delay, throughout and jitter). The approach is based

on a combination of a sampling technique and

passive monitoring method. It can estimate not only

the actual performance of individual users and

applications but also the mixed performance

experienced by these users. The estimation of mixed

users performance will be one of the issues raised in

future work of this study.

This rest of this paper is organised as follows:

Section 2 details the theory behind the sampling

techniques. Section 3 details the mathematical model

of the proposed approach. Section 4 presents the

measurement approach used to validate the proposed

approach. Section 5 illustrates the experimental

results produced. Section 6 is the conclusion.

2 SAMPLING TECHNIQUES

The use of sampling techniques provides

information about a specific characteristic of the

traffic. Sampling methods can be characterised by

the sampling algorithm used, the trigger type (i.e.

count-based or time-based trigger) for starting a

sampling interval and the length of the sampling

interval (Zseby, 2002):

1- Sampling algorithm: this describes the basic

procedure for the process of samples selection.

There are three basic processes: systematic

sampling, random sampling, and stratified sampling.

a) Systematic sampling: It describes the

procedure of selecting the starting point and

the frequency of the sampling according to a

pre-determined function. This includes for

example the periodic selection of every n

element of a trace. Figure 1 shows the

schematic of the systematic sampling

(Claffy et al., 1993).

Figure 1: Schematic of systematic sampling.

b) Stratified sampling: This method splits the

sampling process into multi-steps. First, the

elements (packets) of the parent population

are grouped into subsets in accordance to a

given characteristics. Then samples are

randomly taken from each subset. Figure 2

illustrates the schematic of the stratified

sampling [5]. For example, if the whole

region of interest, A, is spilt into M disjoint

sub-regions (i.e. buckets) such that

(

Bohdanowicz and Weber, 2005):

regionsubktheisAwhere

jlforAAwithAA

−

≠=∩=

∪

Figure 2: Schematic of stratified sampling

c) Random sampling: Random sampling

selects the starting points of the sampling

interval in accordance to a random process

[4]. The selections of sampled elements are

independent and each element has an equal

probability of being selected. Figure 3

depicts the schematic of the random

sampling (Claffy et al., 1993).

Figure 3: Schematic of random sampling

2- Sampling frequency and interval length:

Sampling techniques can be differentiated by the

event that triggers the sampling process (Zseby,

2002, Claffy et al., 1993 and

Bohdanowicz and Weber,

2005). The trigger determines what kind of event

starts and stops the sampling intervals. With this, the

sampling frequency and the length of the sampling

interval (measured in packets arrived or elapsed

time) are determined.

3 THE ESTIMATION CONCEPT

This method was used in (Aida et al., 2002 and

Ishibashi et al., 2004) to estimate the actual delay

experienced by a network user and by mixed

applications based on active measurement using a

change-of-measure framework. By change-of-

measure framework, the authors meant a framework

in which the measure of network performance for

(1)

ESTIMATION OF THE DISTRIBUTIONS OF THE QOS PARAMETERS USING SAMPLED PASSIVE

MEASUREMENTS TECHNIQUES

325

probe packets can be converted to a measure for user

packets. In this paper, the concept of this method

will be used to estimate QoS parameters but based

on a combination of passive measurement and

sampling techniques. The mathematical approach

will be modified to include the sampling technique.

Suppose a network under consideration is shared

by K users and let X

(n) denotes the measurement

objective of the nth packet of user k. X has the

distribution function of P. The distribution of X may

be written as:

{}

[]

aXP

xdPaX

∫

)(1)Pr(

where (a) is an arbitrary real number, E[.] is the

expected value and 1

{.}

denotes the indicator

function:

{}

⎩

⎨

⎧

〉

otherwise

axif

If there are n packets arrived in a measurement

period, X(i) denotes the i

value of X. Then the

estimator Z

(n,a) of the distribution of X, which is

like the mean estimator, is given by:

()

(){}

∑

aiXX

anZ

Suppose a situation in which it is difficult to

measure the user traffic directly and an estimate of

its distribution cannot be obtained. Let V(t) be the

network performance at time t such that if the i-th

arrival packet occurs at t

; then V(t

) = X(t

). Also, let

Y be the sampled version of V(t), and let the

distribution function of Y be Q. Thus, Y is

considered the network performance as measured by

sampled packets and the distribution of Y to estimate

the distribution of X. The distribution of X can be

rewritten by using a change of measure based on the

distribution of Y as follows:

{}

()

{}

()

{}

()

⎥

⎦

⎤

⎢

⎣

⎡

=≅>

>≅=>

∫

YdQ

YdP

EydQ

ydQ

ydP

thenaXydPaY

aYQay

11Pr

;;Pr)(1)Pr(

Now, suppose n user- packets are sent and Y

packets are measured (sampled) m times. Let Y(j) be

the j-th measurement sample at s

such that Y(j) =

V(s

), j=1,2,3...m. Then an estimator Z(m,a) of

Pr(X>a) can be derived by using Y(j) as follows:

()

{}

()

{}

()

(){}

()

()()

jYdQ

jYdP

jLwhere

amZ

SoamZ

YdQ

YdP

mYdQ

YdP

EaX

ajYY

aYaYQ

⎥

⎦

⎤

⎢

⎣

⎡

≅>

∑

;

;,,1

1Pr

L(j) is the ratio between the probabilities of X and Y.

It is called the likelihood ratio, which can be

obtained through passive measurement, in which

simply it is the count of the number of user packets

arriving between the consecutive sampled packets.

Let ρ

(t,δ) be traffic volume (i.e. the number of user

packets) arriving in an interval [t, t+ δ(t)] and let

(t,δ) be the number of measurements (i.e. the

number of sampled packets) in the interval [t, t+

δ(t)]. This indicates that one measurement (sample)

of Y in that interval can be interpreted as

(t,δ)/ρ

(t,δ). So, L can be rewritten as the ratio

between the distributions of the user packets

received at a given period to the distribution of the

sampled packets in that period:

()

(

)

()

∑

δρ

Both ρ

and ρ

are the number of packets at the

given period. Thus the likelihood ratio can be

obtained by passive measurement. Therefore, the

distribution of X is estimated as:

() ()

()

(){}

()

∑

∑∑

δρ

=δ

ajYY

,δsρ

m,aZ

msρandnsρ

bewill)6(inthissubsituteand

);7(fromthenbecause

4 MEASUREMENT APPROACH

The following section describes the use of sampling

techniques for measurements with two monitoring

points. In this work, an evaluation of the user and

network performance by measuring the user QoS

parameters is carried out. A performance

measurement method for estimating the actual

network QoS parameter experienced by the network

users has been proposed based on a sampling

technique. This is based on a passive monitoring

approach. The basic procedure is as follows: 1) Take

a suitable number of samples of the on-going current

traffic, 2) Measure the network performance based

on measuring the QoS parameters (delay, jitter, and

throughput) using the sampled packets, and 3)

Convert the sampled user version to represent the

(2)

(3)

(4)

(5)

(6)

(8)

(9)

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ICETE 2005 - SECURITY AND RELIABILITY IN INFORMATION SYSTEMS AND NETWORKS

326

actual performance experienced by the user packets

by weighting the performance with the number of

user packets arriving between the sampled packets,

which is measured passively.

Some metrics require correlation and

synchronisation of data packets from different

monitoring points like delay. This work was based

on simulation, thus correlation was only considered

by recognising the packets at the second monitoring.

This can be done using packet-ID recognition

(Zseby et al., 2003). Both, correlation and

synchronisation must be considered in real network

The method described, above, was used to

estimate the actual end-to-end QoS parameters. To

demonstrate the application of this method, network

simulator ns2 was used (NS, 2005). Figure 4 shows

the network topology used for the simulation with

the same characteristics of the users as shown in

Table 1. It has three pairs of source/destination

hosts. Sources (N0, N1, and N2) were connected to

their destinations (N5, N6, and N7), respectively,

through two bottleneck routers (N3 and N4), which

are connected with each other via 2Mb/s link. All

the estimations will be done for user1. Other

simulation characteristics are as follows:

• The user's packets were generated by ON-

OFF negative exponential source. ON-OFF

means that the packets are either sent at full

rate with constant burst rate during the

"ON" period or not at all during the OFF

period. For these simulations, the mean ON

duration is set to 1 second and the mean

OFF duration is set to 5 seconds with

selected packet sizes and generation rates

for each application as shown in Table 1.

• The transport protocol was UDP protocol.

• Simulation time was 100 seconds.

Table (1): User's Characteristics

Figure 4: Network Topology

Let X

be the actual user QoS parameter to be

estimated and Y

is the measured parameter using the

sampled packets. The number of packets for user k

arriving in [s

, s

j+1

] is ρ

(j), and the number of total

packets for user k is:

()

∑

Then because there are one sampled packet in the

period [s

, s

j+1

] and ρ

user packets during that

period, and substituting this in equation (8), the

likelihood ratio will be:

() ()

jjL

Substituting equation (11) in equation (8), the

estimate of the user parameter based upon the

sampled packet is:

()

(){}

()

∑

kaiY

amkZ

Thus, by counting the number of user packets

arrived between two consecutive sampled packets,

the QoS parameters can be estimated. As an

example, the count-based trigger frequency was 50

packets for systematic sampling and 50 buckets for

stratified sampling.

5 EXPERIMENTAL RESULTS

5.1 Delay Estimation

An application of this method is to estimate the end-

to-end delay for the network or for a specific user.

The end-to-end delay for user1 will be estimated.

Figure 5 shows the delay distributions of the actual

user and an estimation of the user packet delay based

on the sampled packet using equation (12). It is clear

from Figure 5a that both the distribution of the

sampled packet delay and that of the estimated have

the same distributions. In addition, it can be seen

that the minimum user delay is about 22 msec using

the proposed method. This is equal to the minimum

value from the actual user delay distribution, which

is 22 msec. The maximum estimated delay value is

about 78 msec which is very close to the value from

the actual distribution which is about 80 msec. From

this it can be concluded that the user delay range is

between 22 and 80 msec. Therefore, in the case that

it is difficult to measure the actual delay range (or

the actual delay distribution); it is easy to obtain it

from the estimated one.

Figures 5b and 5c depict the distributions of the

measurements using the random and the stratified

sampling methods. In addition, from the Figures it is

obvious that the two estimation methods produce a

good representation of the actual user packet delay.

User Packet Size

[byte]

Generation Rate

[Mbps]

1 (N0) 600 1

2 (N1) 900 1.2

3 (N2) 800 1.2

(10)

(11)

(12)

ESTIMATION OF THE DISTRIBUTIONS OF THE QOS PARAMETERS USING SAMPLED PASSIVE

MEASUREMENTS TECHNIQUES

327

They gave also the same minimum and maximum

delay values as the systematic sampling for the

actual user delay. In addition, in the figures there are

some discrepancies between the sampled packet and

the actual user estimations that is due to the number

of sampled packets are small compared with the

number of the user traffic packets. Also, it is clear

that the discrepancies between the two distributions,

using the random sampling, are less than the other

sampling methodologies.

5.2 Throughput Estimation

Another application of this method is to estimate the

throughput of a specific user. The end-to-end

throughput of user1 will be, next, estimated.

Figures 6a, 6b, and 6c illustrate the distributions

of the actual user throughput and the estimated

throughput using equation (12). Figures show that

the sampled distribution versions produce good

representations of the actual user throughput. In

addition to that, all of them give an estimate of 1

Mbps of the user throughput which is the real

transmission rate of the user1.

Moreover, from these figures, it can be noticed

that there are some discrepancies between the actual

throughput distribution and the estimated one using

the systematic and stratified techniques. However,

the random sampling approach produced a very

accurate estimation of the actual throughput

distribution compared with the other two

approaches. Therefore, in cases of difficulties in

measuring the maximum throughputs and in

producing the estimate of actual throughput

distributions of a specific traffic, this method can

grant accurate measurement results.

5.3 Jitter Estimation

Here, the end-to-end jitter for user1 will be

estimated. Figures 7a, 7b, and 7c depict the jitter

distributions of the actual user packets and an

estimation of the user packet jitter using the three

sampling techniques using equation (12).

From these figures, it can be observed that all the

distributions produced by the three sampling

methods provide good illustrations of the actual user

jitter. It can be seen the discrepancies are also

obvious in the jitter estimation in both the systematic

and stratified sampling approaches. The random

sampling method produced a more accurate

distribution, which stands for the actual user jitter

distribution. From all distributions, it can be

estimated that the minimum and the maximum jitter

are 0 and 4.4msec respectively.

0 10 20 30 40 50 60 70 80 90

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [msec]

Pr(Delay>a)

User distribution

Estimated distribution

0 10 20 30 40 50 60 70 80 90

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [msec]

Pr(Delay>a)

User distribution

Estimated distribution

0 10 20 30 40 50 60 70 80 90

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [msec]

Pr(Delay>a)

User distribution

Estimated distribution

Figure 5: User delay and estimated user delay distributions using: (a) systematic, (b) random and (c) stratified sampling.

0 50 100 150 200 250 300 350 400 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [Kb/sec]

Pr(Throughput>a)

User distribution

Estimated distribution

0 50 100 150 200 250 300 350 400 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [Kb/sec]

Pr(Throughput>a)

User distribution

Estimated distribution

0 50 100 150 200 250 300 350 400 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [Kb/sec]

Pr(Throughput>a)

User distribution

Estimated distribution

Figure 6: User throughput and estimated user throughput distributions using: (a) systematic, (b) random and (c) stratified

sampling.

(a)

(b)

(c)

(a)

(b)

(c)

ICETE 2005 - SECURITY AND RELIABILITY IN INFORMATION SYSTEMS AND NETWORKS

328

0 1 2 3 4 5

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [msec]

Pr(Jitter>a)

User distribution

Estimated distribution

0 1 2 3 4 5

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [msec]

Pr(Jitter>a)

User distribution

Estimated distribution

0 1 2 3 4 5

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

a [msec]

Pr(Jitter>a)

User distribution

Estimated distribution

Figure 7: User jitter and estimated user jitter distributions using: (a) systematic, (b) random and (c) stratified sampling.

6 CONCLUSIONS

This work highlights the deployment of sampling

techniques for estimating of QoS parameters of an

ON-OFF exponential traffic. Experiments were

performed with systematic, random, and stratified

sampling. These methods showed how the

estimation of the end-to-end QoS parameters could

be achieved using two monitoring points without the

necessity for calculating the whole QoS parameter

population using sampling technique. Also, this

method had the advantage, over the active method,

of not adding an extra load to the network. In

addition, unlike the passive approach, which

requires the transfer and calculations of the whole

traffic data.

From this study, it could be concluded that all

three sampling methods provided an accurate

measure of the QoS parameters. It was obvious that

this method produces an acceptable estimation of

QoS parameters. Nevertheless, for the network

topology and traffic conditions used, the random

sampling showed the best overall performance

because it, randomly, selects the packets for

sampling, which will represent the random

conditions of the network. This could estimate not

only the actual performance of individual users and

applications but also the mixed performance

experienced by these users. The estimation of mixed

users performance will be one of the issues for

future work in this study.

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(b)

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ESTIMATION OF THE DISTRIBUTIONS OF THE QOS PARAMETERS USING SAMPLED PASSIVE

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