Routing algorithms are the key elements in determining the network performance. Therefore, in this thesis a model of logical topologies in the software has been proposed to examine the performance of the logical topologies and their routing algorithms for large scale packet networks. A number of topologies are investigated using the model 2 x 2 node. Different routing protocols are used for forwarding packets in network. Routers keep up with a routing table for successful delivery of the packets from the source node to the destined node. Most of the popular routing algorithms used are RIP, OSPF, IGRP and EIGRP. In this paper we proposed an intelligent routing algorithm by using AntNet algorithm for best effort IP networks, and tested this proposal algorithm to check its performance.

1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 111-117 © IAEME
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 6, June (2014), pp. 111-117
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IJCET
© I A E M E
A PROPOSAL INTELLIGENT ALGORITHM FOR ADAPTATION OF ANT-NET
ROUTING IN BEST EFFORT IP NETWORKS
Dr. MunaMohamedJawad
Computer Engineering Dept., The University of Technology, Baghdad, Iraq
Dr. MahmoodZakiAbdullah
Computer Software Engineering Dept., Al-Mustansiriyah University, Baghdad, Iraq
AhmedNahidhHamzah
M.Sc. Student, Computer Engineering Dept., The University of Technology, Baghdad, Iraq
ABSTRACT
Routing algorithms are the key elements in determining the network performance. Therefore,
in this thesis a model of logical topologies in the software has been proposed to examine the
performance of the logical topologies and their routing algorithms for large scale packet networks. A
number of topologies are investigated using the model 2 x 2 node. Different routing protocols are
used for forwarding packets in network. Routers keep up with a routing table for successful delivery
of the packets from the source node to the destined node. Most of the popular routing algorithms
used are RIP, OSPF, IGRP and EIGRP. In this paper we proposed an intelligent routing algorithm by
using AntNet algorithm for best effort IP networks, and tested this proposal algorithm to check its
performance.
Keywords: IP Networks, RIP Algorithm, OSPF Algorithm, IGRP AntNetAlgorithm.
I. INTRODUCTION
One of the fast growing issues is grown of networks where the conditions of traffic are
occasionally changed and failed which occur at some parts of network in an unpredictable way.
Therefore, researchers tried to find an algorithm that can manage traffic flows and deliver packets
from the source to the destination in a realistic time, where that routing algorithm could be the key
element in network performance and reliability, and considered as the “brain” of the network that

2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp.
111-117 © IAEME
0976-6367(Print),
manages traffic through the network. One of the ways to perform this could be by using intelligent
routing algorithm. Such as, Genetic Algorithms [1], Neural Networks [2], Software Agents [3] and
Reinforcement Learning [4]. The most common and well
shortest path and bellman ford algorithms. There are protocols related to these algorithms. Some of
these protocols are: Open shortest path first (OSPF) that is deployed throughout the networks and the
de facto routing algorithm within the networks. Ex
protocol which is mainly used between the backbones and as its name suggests, among the gateways.
However, nowadays most widely used gateway protocol is border gateway protocol (BGP)
Traditional routing algorithms mentioned in previous section lake intelligence, and needs human
assistance and human interpretation in order to adapt to failures and changes. Therefore, agent based
systems and reinforcement learning have attracted researches because these methods don’t
supervision and are distributed by default. For example Q
particularly ant based systems [6].The main objective of networks and data communication is to
exchange data between devices. For this reason the s
types. Large scale network refers to network with a lot of devices combined for achieving network
tasks [7]. Large scale networks could be in any topology or could be in hybrid of topologies and
works with protocols required to rule networks , for routing , each time the network gets bigger,
problems appears and be bigger; For a large scale network size, the task becomes both complicated
and time consuming. The aim of the work in this
algorithm for the large scale networks
II. THE PROPOSED SYSTEM
A. Node Model
Dijkastra’s
Fig (1.1) shows a logical node structure based 2 x 2 routers that will be used as the node
model in the simulations. As shown in
node on request and of course on availability of output port.
B. Packet Format
The packet format is supposed to be fixed, combined of a header and payload as shown in
fig. (2). The header holds the required information for the packet routing algorithm. It is composed
of number of hops known by packet (packet age), a unique packet identifier and the packet
destination address. It is assumed that the packet is delayed while t
each node. Although it is assumed that there is sufficient time for header processing, packet header
content should be kept to a reasonable size in order to ensure faster processing, thus increased
throughput. In practical systems, if a packet cannot be processed within the given time (in the
dedicated time slot) then it is regarded as a lost packet, since there is no mechanism to store partial
routing information. The packet payload carrying the data has no effect on the rou
112
twork. well-known routing algorithms are Dijkastr
Exterior gateway protocol (EGP) is a gateway
Q-learning methods [5], Swarm intelligence
scale of networks differs in size, topologies, and
]. paper is to design and implement an intelligent
networks.
fig. (1), packets can be inserted (added) or dropped at the
Fig .1: A2×2 router
the header is being processed at
ystems, routing decision.
terior BGP).
need any
cale he ting

3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp.
Fig .2: Packet format
C. Network Routing Algorithm Framework
111-117 © IAEME
0976-6367(Print),
A framework for the routing algorithm implementation and simulation is given in
fig. (3).
This model is based on the information gathered from the review of the networks and the routing
algorithm properties and will be used as the base model for implementing the routing algorithm of
the selected networks. The design and implementation pro
outline below:
!

6. Fig .3: Routing algorithm interconnection network framework
113
ramework
process cess are broken down into eight main steps

7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 111-117 © IAEME
D. Simulation Test of A proposed Logical Network
'(') '('* '(' '() '(* '(' '(+ '()) '(+' (''
#, $#, $', )%#, %-$,
114
For this test the following criteria have been selected to evaluate the performance of the
routing algorithms for different network sizes under different system loads:
1. Average number of hops: The performance analysis and best/average/worst case comparison of
the algorithms is determined according to the following. Average number of hops per packet
Nh-p from source to destination is defined as [8]
(1)
Where NT is the total number of packets, and NH is the total number of hops.
2. Contention percentage: Contention occurs when two or more packets are intended for the same
output port. Congested packets are either buffered or deflected to the next available port. The
contention ratio c' which measures this is defined as:
(2)
Where Nc is the total number of contentions that occurred.
III. RESULTS
A. Average Number of Hops
System load is compared to the average number of hops, for different logical network sizes
shown in fig. (4).
'
%
$
#
'
%
$
#

8. Fig .4: system load against average no. of hops for logical network with deflection mode (no-buffer)
As can be seen from the figure, the number of hops increases very little with the system load
for small size networks (24N and 64N), whereas for large size network, the increase is linear for the
logical networks. This shows that for a large size networks, they are scalable and can cope with
increasing traffic when there is no buffer. When a buffer is employed in the nodes, the performance
of the networks and their routing algorithms increase dramatically, see fig. (5).

9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 111-117 © IAEME
'(') '('* '(' '() '(* '(' '(+ '()) '(+' (''
#, $#, $', )%#, %-$,
'(') '('* '(' '() '(* '(' '(+ '()) '(+' (''
#, $#, $', )%#, %-$,
115
'
-
%
*
$
+
#
)

10. Fig .5: System load against no. of hops for logical networks with store-and-forward mode
(with buffer)
Table (1.1) shows the Comparing deflection routing (without-buffer) tests with the store-and-forward
(with-buffer) routing tests that there is an improvement in performance in the latter case.
However, this improvement is gained at the cost of system complexity.
Table.1: Comparison the average no. of hops for two types with different networks sizes.
Average number of hops
Network size 24(Nodes) 64(Nodes) 160(Nodes) 384(Nodes) 896(Nodes)
With buffer 3.15 4.73 6.22 7.84 9.59
Without buffer 3.66 6.00 8.77 12.31 16.70
B. Contentions
Fig. (6) shows the contention ratio versus the system load for deflection routing (without
buffer) for different network size. Contention ration increases slightly and gradually because these
networks can act as a 'buffer' when there is a need for buffering, where system loads increase
relatively to the size of the network.
''
%'
$'
#'
'
''
%'
$'
#'
'
'
Fig .6: system load against contention ratio for logical network with deflection mode (no-buffer)

11. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 111-117 © IAEME
'(') '('* '(' '() '(* '(' '(+ '()) '(+' (''
#, $#, $', )%#, %-$,
116
Fig. (7) shows that the contention percentage increases when store-and-forward (with buffer)
routing is employed. In addition, the number of packets where contention occurred has increased for
all networks when the number of nodes and the system load are increased in kind of exponential
manner.
+'
''
+'
''
+'
'
Fig.7: system load against contention ratio for logical network with store-and-forward mode
(with-buffer)
More packets experienced contention as compared to the other two. Comparing results for the
store-and-forward routing with the deflection routing shows that there is an increase when there is
buffer, this is because when two packets are at the input ports destined to the same output port and
when there is a packet already in the buffer that is also wanting to go to the same port regardless of
the type of the packet (Multiple or Single), the packet in the buffer is forwarded to the output port.
Table (1.2) shows that packets on average experienced have more contention in the when store-and-forward
routing is employed compared to other deflected.
Table.2: Comparison the Contention Ratio for two types with different networks sizes
Contention Ratio (%)
Network size 24(Nodes) 64(Nodes) 160(Nodes) 384(Nodes) 896(Nodes)
Without
18.85 36.19 56.95 77.90 109.70
buffer
With buffer 22.30 36.90 58.70 81.20 107.40
IV. CONCLUSIONS
The proposed algorithm is implemented over logical networks switches with two types of
strategies (deflection, Store-and-forward), those types are implemented under different network sizes
to show the performance of the proposed algorithm with scalable networks with those two types of
strategies, the algorithm shows good performance when store and forward strategy implemented
because of buffer existences that holds the packet and forward the others. Future work approaches
can also be used, where worker ants representing data packets within the network can only change
the pheromone levels of path that they travel and also use low priority queues. However, beside the

12. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 111-117 © IAEME
worker ants there can be other ants with higher priorities queues carrying high priority packets that
can travel faster (using high priority queues) within the network and be able to update more than one
entry in the routing table.
117
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