Modeling of Measurement-Based Admission Control Algorithm Using OPNET
A. Asars1, M. Kulikovs2, E. Petersons3
1
TET Department, ETF Faculty, RTU
Riga, Lomonosova str. 1, LV-1019, E-Mail: a.asars@inbox.lv
2
TET Department, ETF Faculty, RTU
Riga, Lomonosova str. 1, LV-1019, E-Mail: mike@dimir.lv
3
TET Department, ETF Faculty, RTU
Riga, Lomonosova str. 1, LV-1019, E-Mail: ernests.petersons@rtu.lv
The present paper aim was to analyze effectiveness of admission
control algorithm in the environment with self-similar traffic.
For this purpose we introduced the model of measurement based
admission control (MBAC) algorithm of network node. Decision
making process in the MBAC algorithm is based on the analysis
of input flow and anticipate the impact of new connection on the
whole system performance. The results of entropy analysis
approach are used to link measurements to the forecasting model
of network node performance.
Our simulation results show that by using classical methods
methods to avoid buffer overflow are not suitable for self-similar
traffic.
Keywords: QoS, measurement-based admission control,
performance evaluation.
I. INTRODUCTION
Real network systems have limited capacity, limited
bandwidth and quality. Total performance of a system can
be increased by means of Quality of Service (QoS)
mechanism. QoS can provide different priorities to different
users or groups of users, as well as for different kinds of
data flow, e.g. VoIP or WWW. In order provide such
service the mechanism should have two main
functionalities. The ability of the mechanism to estimate the
level of QoS that a new user session will need. And the
ability to confirm that there is enough bandwidth available
to service the session.
It means that there will be a competition among
sessions and flows for network bandwidth. The mechanism
that manages the decision whether a new connection should
be admitted to network or not is called “admission control”.
The easiest admission control method is to serve flows
basing on arrival time.
An alternative is parameters-based admission
control where decision is being made taking into account
the parameters of the traffic. A well known parameter-based
admission control is measurement-based admission control
(MBAC) which makes decision based on parameters
obtained from existing traffic measuring. Every MBAC
consists of two components: a measurement procedure and
an admission decision algorithm. The measurement
procedure should be designed taking into account two
principles. Firstly, the procedure should measure traffic as
often as possible to have enough data to make the right
decision. Secondly, the measurements should not overload
the system with themselves.
Different algorithms were presented in previous works.
The aim of the present work is first, to express MBAC
admission controls method in terms of OPNET simulation
tool. And the second aim is the evaluation of the efficiency
of MBAC algorithms in two scenarios, e.g. data flow with
Poisson and self-similar characteristics. The paper presents
the evaluation of the admission decision algorithm, based
on buffer overflow probability for GI/M/1 model. The
evaluation is done based on the OPNET simulation results.
The chapter of theoretical background describes
the OPNET model that is studied in the paper. It consists
of four sections: description of the model, description of
the traffic generator in terms of OPNET model, description
of parameters. The last section in the chapter presents the
description of MBAC model used in the paper. Moreover,
the section presents the MBAC model in terms of the
OPNET.
Further, the results gained during the simulation
for Poisson and self-similar data flow are presented. We
use simulation on several network scenarios, with and
without admission control.
The discussion and analysis of the results,
conclusion and our vision of further possibilities for
studies of a current problem are presented in the final part
the paper.
II. Theoretical Background
A. Description of The Model.
In the present paper we evaluate algorithm
effectiveness for an admission control for different types of
data flow using OPNET software tool. Admission control,
presented in this paper, is applied for two types of data
flow, Poisson and self-similar. The Poisson data flow is
modeled based on the assumption that the size of packets
and interarrival time are distributed by exponential
distribution low. The self-similar data flow is modeled
based on assumption that packet size and interarrival time
are distributed by Pareto distribution law.
Data flow model that is used in the paper is the
following. In the paper we have four data flow generators.
Each data flow consists of, so called, sessions. The session
is the batch of packets with predefined average arrival
intensity (λ). Different sessions of the one data flow
generator can have different λ. In our model of the
measurement-based admission control the parameter λ is
used to make decision grant or devote a new session
connection. The algorithm is described in the following part.
B. Traffic Generator.
The data flow generator consists of tree units. It gives us
flexibility to simulate different types of data. These nodes in
some sense are similar to simplified OSI model and
correspond to different layers. At the Fig.1 a simplified
network model, in terms of OPNET, is presented.
admission control (AC) mechanism is represented there.
This model presents a well known model, called M/M/1.
The model consists of four data generators,
network buffer and a data channel. Network queue (the
queue unit) and network (processor unit) are depicted on
Fig.1.
a)
b)
L(x)=pdf
c)
λ= pdf
Fig. 2. Data flow creation method in OPNET. a) file creation; b) data
packets creation; 3) data packet with delays
Fig. 1. The network model without admission control
The first unit of the data flow generator is a
processor unit that is responsible for a session creation. The
unit generates a big data array, for instance, the file. That is
why the name of the unit was chosen as “file_generator”. At
the Fig.1 it is the first unit in the row. The file size, in terms
of this paper, is one of the parameters that define the
duration of the session. The size of the file can be generated
using different distribution laws. In our model for file
generation we used exponential distribution law with 10240
mean values.
After the file is generated, it flows through packet
creator unit. In terms of OPNET this unit is presented with
processor. The unit divides the file for packets. Packet size
distributed based on the distribution law and is promoted to
process node from the higher layer – network node. In the
model two different distribution lows for size of the packets
we promoted. We used exponential and Pareto distributions.
The last unit in the data flow generator is packet
queue. This unit, in terms of OPNET is presented as a queue
and adds the delay between the packets. The delay between
the packets is defined in the same way as the size of the
packet, i.e. is promoted from higher level. The delay
between two packets in OPNET is implemented as service
time that the first packet spent in the queue to be treated.
The Fig. 2 presents an instance of the output of the
data flow generator. It presents the file, generated by
file_generator unit. The Fig. 2 b. presents the job of the
packet creator unit. At the bottom of the Fig. 2 the delays
between the packets that add a packet queue unit are shown.
To compare efficiency of the algorithm, two
models of the network were used. Returning to the Fig.1, it
is seen, that the simplified model of the network without
There are two big differences in our model
between packet queue and network queue units. The packet
queue is implemented as an infinite size queue, while
network queue is designed as a queue with limited packets
capacity. Such a difference can be logically explained.
Packet queue represents the client side of transaction,
where information before transaction can be stored in any
kind of storages. The network_queue represents network
equipment, where there is only one kind of storage place.
Usually, the client side has much more storage capacity
than network equipment. That is why infinite and finite
models of the queue were chosen.
The second difference between queues is the
following. The service rate parameter for the
network_queue unit is the constant value, while service
rate of packet_queue unit is defined by distribution law
which depends on the kind of simulated traffic (either
Poisson or self-similar traffic).
In this part of the present paper the model
structure was described in terms and notation of OPNET
simulation software. It was shown, how M/M/1 model cam
be simulated using OPNET. In the following parts we
describe the parameters for the model.
C. Description of Parameters
As it was mentioned above the model should be flexible
and scalable. The model should be designed so that it could
be used within bigger models, collaborate with other
models and with different kinds of scenarios.
The model was designed according to the rule
mentioned above. Units of the model are supposed to get
parameters from the higher level of abstraction. This
principle is implemented in the model using a build-in
functionality, which is called promote parameters.
The example of the model could be as the
following. The model should simulate two different kinds
of traffic: Poisson and self-similar. It means that two
different distribution laws need to be used. There are two
possibilities. The first one is a direct definition of the
distribution law for each unit within model domain. It
means that the distribution law of the packet_generator
unit, described in the previous part of the chapter, should
be defined in the node domain. And what is worse, that
parameters of the packet_queue should be changed in the
process domain. Does this approach provide us with
comfortable work? Does it satisfy the rules mentioned in the
first paragraph? There could be some scenarios where there
is no other possibility than direct definition of the
parameters for the units. In all other cases the second
approach should be used.
The second approach to define parameters for the
units is the promotion of parameters from the higher level of
the model abstraction to the lower one. It means that as
much as possible parameters should be defined in the
network domain of the model. Units, defined in the model
before the simulation, get the necessary parameters from the
higher level of the abstraction. For the model described
above it means that the packet_generator processor would
divide the file according to the distribution law. i.e. it will
receive from the network domain. In the same way the
packet_queue would get definition of the distribution for
making delays between packets and would transfer the
definition to process domain of the packet_queue unit.
Such a promotion of the parameters provides the
model with greater flexibility and gives an opportunity to
use this model for the other bigger, more difficult models
and scenarios.
An example of parameters promotion is presented
on Fig 3. At the Fig. 3.a. promoted parameters for the
Poisson data flow simulation are shown. For simulation of
the Poisson data flow the size of the transferred packets are
defined by exponential distribution with the mean value of
512 bit. At the figure such a definition is represented by the
attribute “packet_creator.Packet Size” and the value
“exponential (512)”. Another necessary requirement for
Poisson data flow simulation is that the delays between two
packets should be distributed according to exponential
distribution law. The Fig 3.a. presents the promotion of
exponential distribution for the delays with the mean value
if
the
delay
equals
6600.
An
attribute
“packet_queue.Service Rate” and the value “exponential
(6600)” promote delays parameters to the packet_queue
unit.
flow, simulated and presented in the paper, with selfsimilar characteristic has the following parameters. Packet
size is distributed according to Pareto distribution size. The
delay between two packets was defined according to Pareto
distribution law.
As we mentioned above, for self-similar traffic
simulation the Pareto distribution law is used. The Pareto
distribution has two parameters for the definition. They are
α – the shape parameter and κ - the parameter referred to a
scale. The Pareto distribution is used in application to the
self-similar traffic model description because one of the
properties of the Pareto distribution is one of the most
common heavy-tailed distributions. And the self-similar
traffic has heavy-tailed characteristic and can be modeled
with a heavy-tailed distribution. Informally, self-similar
traffic means that the shape of the traffic is invariant with
respect to the time scale.
Fig. 3b. Parameters promotion for self-similar data flow simulation.
The model, described in the paper, for the self-similar
traffic description uses the following parameters. These
parameters are represented on Fig. 3, b. and are the
following. For the creation of the packet Pareto distribution
with κ–512 and α–128 is used. And for the delay providing
between packets Pareto distribution with κ–512 and α–128
is used.
C. MBAC Algorithm
Fig. 3a. Parameters promotion for Poisson data flow simulation.
The Fig. 3, b. represents promotion of the
parameters for self-similar data flow simulation. The data
As we discussed before, the main idea of the model
presented in the paper is evaluation of the buffer overflow
for Poisson and self-similar traffic. The evaluation consists
of two scenarios. In the first scenario the traditional
network model is used for the simulation. While the
second scenario supposes to have admission control
mechanism for decreasing buffer overflows probability.
This part presents the algorithm of the admission
control mechanisms. It is important to emphasize that the
admission control mechanism described in the paper is
measurement-based admission control. The paper presents
the simpliest measurement admission control algorithm. It
was used as a first step to find out how do self-similar
characteristics of the traffic impact on the buffer overflow.
Taking into account that the simplest measurementbased admission control is going to be implemented, the
algorithm of the traffic measurement was not used.
Measurements of the traffic intensity are simply recorded
every five time units and in terms of admission decision
correspond to the real current traffic intensity ( current ).
The theoretical session intensity (  new _ sesion ) is
defined during packet creation procedure and is used by
admission control mechanism for making a decision. If the
acceptance of the new session influences the total traffic in
the way such a threshold of the decision border is lower
than the defined one, the new connection will be accepted.
Otherwise it will be rejected.
An algorithm of making the decision, used in the
paper, is the following. The total intensity 
of traffic
 - interarrival intensity;  - service intensity; 
– utilization;  k – probability, that there are exactly „k”
where
jobs in the system.
In the term of the admission control mechanism, if
the probability, that there are exactly K jobs, where K
equal to the size of buffer, is higher than 0.7 a new
connection establishment will be rejected.
For the simulation and effectiveness evaluation of
this method the following model is introduced. In the Fig.
4. the OPNET model of the network with AC is shown. As
you can see, there is addition node comparing to usual
network model presented in Fig.1. This node is called AC.
Exactly this node realizes the admission control
mechanism.

equals the sum of the real intensity of the traffic
current
and a traffic intensity of the new session  new _ sesion :


 current  new _ sesion
In the paper a threshold of the new connection acceptance is
based on buffer overflow probability. It means that
admission control mechanism will accept a new connection
if the buffer overflows probability with intensity of the
traffic equals to 
will be lower that defined level. In

the paper the level of acceptance based buffer overflows
probability is 0.7. It means that a new connection will be
established only if the probability of buffer overflow is
smaller that 0.7, otherwise the connection will not be
established.
The well known algorithm for buffer overflow probability
calculation is defined by M/M/1/K model. In the model,
described in the paper, admission decision is taken based on
this buffer overflow probability calculation method. In
M/M/1/K model first M means that interarrival time
distributed according to exponential distribution law. The
second M means that service time correspond to exponential
distribution also. 1 in the model description means that we
have a single server. K in the mentioned notation means that
the system can handle only K jobs. This corresponds to the
limited buffer with length of K cells. It is clear seen that the
Fig.1 represents exactly M/M/1/K model. The limitation of
the buffer means, that if there are already „k” jobs in the
queue, the next job will be rejected and the buffer overflow
counter increases.
In the [8] the steady-state probabilities that in the
system are exactly K jobs are obtained and can be written as
follow:
k

  
 k   0     1     

  
or
k
 k  1      k ,
Fig. 4. The model of a network infrastructure with admission control
mechanism.
In the Fig. 4. there are represented two
communication links between dataflow generator and
admission control unit. The communication link from
packet generator to AC node can send two types of the
packets. Before the establishment of a new connection
packet generator sends defined intensity of the flow as an
acknowledgment packet. Receiving the packet to establish
a new connection, AC node calculates current probability
of the overflow. If the probability is less or equal to
defined so, the connection will be established and AC node
sends a packet to packet generator with acceptance of the
session. Otherwise, the connection will be rejected, and
AC node sends rejection packets and initiator of the
session should wait another moment to try to establish the
communication session.
To create an admission control node it was
necessary to define a new object – process. For definition
of the process in OPNET process node is used. Any
process in OPNET is defined by Final State Machine
(FSM). OPNET has two defined kind of state. They are
forced and unforced states. An unforced state is one that
returns control of the simulation to the Simulation Kernel
after executing its entering executives. A forced state is
one that does not return control, but instead immediately
executes the exit executives and transitions to another
state.
The Fig. 5 presents the AC node defined in OPNET
with FSM notations. Green states correspond to forced
states, while red one corresponds to unforced states.
7
6
5
4
3
2
1
3492
3276
3060
2844
2628
2412
2196
1980
1764
1548
1332
900
1116
684
468
36
252
0
Fig. 6b line) the buffer overflow in Poisson data flow in logarithmic
scale; discreet line) buffer overflow in self-similar data flow logarithmic
scale.
B. Buffer Overflow with Admission Control
Fig. 5. AC node definition by FSM notation
In the following chapter results analyze of the described
admission control mechanism are presented.
IV. RESULTS
Statistics about rejected packet was collected using OPNET
software for the Poisson and self-similar traffic. Based on
the results some analysis of admission control efficiency is
given. The first part presents the data for the model without
admission control, while the second part presents buffer
overflow statistics with admission control mechanism.
A. Buffer Overflow without Admission Control
In Fig. 6 charts of the buffer overflow with Poisson and
Self-Similar data flow are presented. Fig. 6.a presents the
buffer overflow chart using a normal scale, while Fig. 6, b
presents the same data using logarithmic scale.
The continuous line shows buffer overflow in Poisson data
flow and the dashed line shows buffer overflow in selfsimilar data flow. As you can see, buffer overflow in selfsimilar traffic if much higher than in case of Poisson traffic.
600000
500000
The Fig. 7 presents buffer overflow statistics. For the vivid
in the chart statistic for both scenarios (with and without
admission control) are presented. Fig. 7, a presents charts
in normal scale, while Fig. 7, b presents in logarithmic
scale.
In the figure, the continuous lines present buffer
overflow for scenario with admission control. The dashed
lines correspond to buffer overflow for scenario without
admission control.
Using the graph it is clearly seen, that the
admission control mechanisms, which was presented in the
paper, gives good result and decreases the number of
rejected packet. The numbers of rejected packets without
and with admission control differ approximately for 20%.
C. Analysis
An advantage of the simplest admission control
mechanism is around 20%. It means that the efficiency of
the admission control algorithm based on traditional model
for the buffer overflow probability shows good result for
both Poisson and self-similar traffic. On the other side, it is
still not enough to reduce buffer overflow to accepted
level.
V. CONCLUSIONS
400000
300000
200000
100000
3492
3276
3060
2844
2628
2412
2196
1980
1764
1548
1332
900
1116
684
468
36
252
0
Fig. 6a line) the buffer overflow in Poisson data flow; discreet line) buffer
overflow in self-similar data flow.
The Fig. 6 specifies that self-similar traffic with heavytailed characteristics of the traffic has great impact on
network parameters, e.g. buffer overflow.
The paper presents an example of the data flow simulation
based on OPNET software tool. Two types of data flow
were simulated. They are Poisson and self-similar data
flows. The buffer overflow dependence on the traffic
characteristic is presented in the paper. It is shown that
heavy-tailed characteristics of the self-similar traffic have
crucial influence of the network parameters, e.g. buffer
overflow. Traditional network models are using
assumption that traffic has to be Poisson-like traffic. The
current paper presents uselessness of the traditional model
for the meantime traffic description.
Simulation of the measurement-based admission
control mechanism, based on buffer overflow probability
calculation algorithm presented in the paper. For the
simulation of the admission control OPNET software was
used. Uselessness of the traditional models, constant
growing variety and difficulty of the network protocols push
to use simulation software for network researches and
development.
7
6
5
4
3
2
1
3492
3276
3060
2844
2628
2412
2196
1980
1764
1548
1332
900
1116
684
468
36
252
0
Fig. 7. Continuous line corresponds to scenario with AC. Dashed lines
correspond to scenario without AC. The two lines on the bottom
correspond to Poisson data flow. The two lines on the top correspond to
self-similar data flow.
VI. FUTHER WORK
The current paper presents the evaluation of the simplest
admission control mechanism. Extension of the admission
control algorithms is the future work. There are two
directions that should be improved. The traffic measurement
algorithm should be traffic dependent. It means, that the
algorithm should be able to analyze the characteristics of the
traffic and to provide optimal measurements of the traffic
based on these parameters. The same algorithm should
provide information about the optimal quantity of
information that is necessary to collect for traffic analyze
and prediction.
The second direction is the development of the
effective decision making algorithm for the admission
control.
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