Many Internet multicast applications have

stringent Quality-of-Service (QoS) requirements

that include delay, loss rate, bandwidth, and delay

jitter. In this paper, we present a Swarm

intelligence based on Ant Colony Optimization

(ACO) technique to optimize the multicast tree .

Solving QoS Multicast

Routing Problem Using

ACO Algorithm

Course of: High speed network

Abdulaziz Tagawy

Solving QoS Multicast Routing Problem Using ACO

Algorithm

Define the problem:

In IP multicasting, there are many applications (such as videoconferencing,

distance education, and online simulation, Access to Distributed Databases, and

Information Dissemination.) are sent messages from the source node to all

destination nodes.

To support these applications, it is necessary to determine a multicast tree of

minimal cost to connect the source node to the destination nodes subject to

delay constraints.

One of the most distinctive aspects of the network routing problem is the

nonstationarity of the problem’s characteristics. In particular, the characteristics

of traffic over a network changes all the time, and in some important cases (e.g.,

the Internet) the traffic can fluctuate in ways difficult to predict. Additionally,

the nodes and links of a network can suddenly go out of service, and new nodes

and links can be added at any moment.

These applications have stringent Quality-of-Service (QoS) requirements that

include delay, loss rate, bandwidth, and delay jitter. This leads to the problem of

routing multicast traffic satisfying QoS requirements. The above mentioned

problem is known as the QoS constrained multicast routing problem and is NP

Complete.

In order to meet QoS requirements an optimizing algorithm is needed.

Therefore; we introduce to use swarming agent based intelligent algorithm

which is a relatively new approach of Ant Colony Optimization (ACO) that

takes inspiration from social behaviors to solve optimization problem.

Literature Review:

Shortest-path routing has a source-destination pair perspective: there is

no global cost function to optimize. Its objective is to determine the

shortest path (minimum cost) between two nodes, where the link costs are

computed (statically or adaptively) according to some statistical

description of the tra‰c flow crossing the links. Considering the different

content stored in each routing table, shortest-path algorithms can be

further subdivided into two classes called distance-vector and link-state

(Steenstrup, 1995).

Distance-vector algorithms make use of routing tables consisting of a set

of triples of the form (destination, estimated distance, and next hop),

defined for all the destinations in the network and for all the neighbor

nodes of the considered switch. In this case, the required topologic

information is represented by the list of identifiers of the reachable nodes.

The average per node memory occupation is in. The algorithm works in

an iterative, asynchronous, and distributed way.

The information that every node sends to its neighbors is the list of its last

estimates of the distances (intended as costs) from itself to all the other

nodes in the network.

It can be noted that this algorithm converges in finite time to the shortest

paths with respect to the used metric if no link cost changes after a given

time (Bellman, 1958; Ford & Fulkerson, 1962; Bertsekas & Gallager,

1992); this algorithm is also known as distributed Bellman-Ford.

Link-state algorithms make use of routing tables containing much more

information than that used in distance-vector algorithms. In fact, at the

core of link-state algorithms there is a distributed and replicated database.

This database is essentially a dynamic map of the whole network,

describing the details of all its components and their current

interconnections. Using this database as input, each node calculates its

best paths using an appropriate algorithm such as Dijkstra’s (Dijkstra,

1959), and then uses knowledge about these best paths to build the

routing tables.

In the most common form of link -state algorithm, each node acts

autonomously, broadcasting information about its link costs and states

and computing shortest paths from itself to all the destinations on the

basis of its local link cost estimates and of the estimates received from

other nodes. Each routing information packet is broadcast to all the

neighbor nodes which in turn send the packet to their neighbors, and so

on. A distributed flooding mechanism (Bertsekas & Gallager, 1992)

supervises this information transmission, trying to minimize the number

of retransmissions.

In recent years, some meta-heuristic algorithms such as the ant colony

algorithm, genetic algorithm, particle swarm optimization, and Tabu

search, have been adopted by the researchers to solve the multi-

constrained QoS routing problems.

In mobile agents for adaptive routing an intelligent routing algorithm

ANTNET based on ant colony algorithm was proposed. Their algorithm

has some attractive distribution features and it can find a near-optimal

path from the source node to each destination node. It also provides the

required results in simulation. Although the ANTNET is a unicast routing

algorithm, it can be easily applied to multicast routing with some

modifications. In spite of the said merits of the ANTNET, it suffers from

a serious drawback i.e. the slow convergence rate.

The genetic algorithm (GA) was used to find a multicast tree satisfying

the constraints of bandwidth and delay with least cost. The GA has three

operators: selection, crossover, and mutation. The individuals are stored

in connective matrices by adopting the binary coding system.

The initial colony is generated randomly without considering QoS

constraints. The selection operation adopts Roulette wheel algorithm to

select the best individuals from the parent generation to pass onto the

child generation. Then, the crossover operation is used to find out the

fittest among the best. A penalty function is adopted to solve QoS

constraints in the multicast trees, which do not satisfy QoS constraints.

Although sometimes the algorithm’s performance is observed to be

satisfactory, still it encounters some faults, such as the local search

ability, premature convergence, and slow convergence speed. Further, the

genetic algorithm does not assure to find a global optimum. It happens

very often when the populations have a lot of subjects.

Researchers have proposed particle swarm optimization PSO algorithms

to solve QoS constraint routing problem. The PSO algorithm proposed

solves the QoS multicast routing problem and can obtain a feasible

multicast tree by exchanging the paths. This algorithm can converge to

the optimal or near-optimal solution with lower computational cost.

Another algorithm based on the concept of quantum mechanics named as

Quantum-Behaved Particle Swarm Optimization (QPSO) was proposed.

Here, the proposed method converts the QoS multicast routing problem

into integer programming problem and then finally solves using the

QPSO. The QPSO finds the path from the source node to each destination

node and constructs the tree by merging the paths. A tree based PSO has

been proposed for optimizing the multicast tree directly. However, its

performance depends on the number of particles generated. Another

drawback of the said algorithm is the merging of multicast trees. The

elimination of directed circles and nesting of directed circles are also very

complex and are considered as some of the limitations of the PSO.

many researchers have solved the QoS constrained multicast tree using

Tabu search [18,19]. A Tabu search method was proposed in [18] to

search for the multicast tree with the least cost that satisfies the

constraints of bandwidth and delay. This algorithm obtains a complete

graph of all group members at the initial step and obtains the initial

Steiner tree via the generated tree of the complete graph. In this way, the

k-shortest paths replace the edges to find the chances of getting better

results. The method mentioned above is similar to the method of path

combination. However, it does not operate directly on the multicast tree.

This weakness makes it impossible to eliminate the constraints of

conventional multicast routing algorithms. Hence, there arises a need to

proceed further and do more amount of work in searching paths and

integrating the multicast trees.

Research Methodology:

Before sending or receiving IP multicast data, a network must be enabled for

multicasting, as follows:

Hosts must be configured to send and receive multicast data.

Routers must support the Internet Group Membership Protocol (IGMP),

multicast forwarding, and multicast routing protocols.

Working:

To support multicasting in an internetwork, the hosts and routers must be

multicast-enabled. In an IP multicast-enabled intranet, any host can send IP

multicast datagrams, and any host can receive IP multicast datagrams, including

sending and receiving across the Internet.

The source host sends multicast datagrams to a single Class D IP address,

known as the group address. Any host that is interested in receiving the

datagrams contacts a local router to join the multicast group and then receives

all subsequent datagrams sent to that address.

Routers use a multicast routing protocol to determine which subnets include at

least one interested multicast group member and to forward multicast datagrams

only to those subnets that have group members or a router that has downstream

group members.

ARCHITECTURE:

Component

Description

Host (source or receiver)

A host is any client or server on the network. A multicast-

enabled host is configured to send and receive (or only send)

multicast data.

Router

A multicast router is capable of handling host requests to join

or leave a group and of forwarding multicast data to subnets

that contain group members. A multicast router can be either a non-Microsoft router that uses a multicast routing protocol or a Windows Server 2003-based server running the Routing and Remote Access service.

Multicast address

A Class D IP address used for sending IP multicast data.

An IP multicast source sends the data to a single

multicast address, as described later in this section. A

specific IP multicast address is also known as group

address.

Multicast group

A multicast group is the set of hosts that listen for a

specific IP multicast address. A multicast group is also

known as a host group.

MBone

The Internet multicast backbone, or the portion of the

Internet that supports multicast routing.

ANT COLONY OPTIMIZATION ALGORITHM:

Swarm intelligence is a relatively new approach to problem solving that takes

inspiration from social behaviors to solve optimization problem. The attempt is

to develop algorithm in computer technology to solve real life problems. Ant

colony optimization is a heuristic algorithm which has been proven a successful

technique and applied to a number of combinational optimization problem and

taken as one of the high performance computing methods.

It has wide range of applications with very good search capabilities for

optimization problems but it still remains a bottleneck due to high cost and time

conversion. ACO inspired by the forging behavior of real ants to find food from

their nest. The algorithm is basically used to find shortest path from nest to food

source and the path is then used by other ants this is all due to chemical name

pheromone deposited by ants on ground while searching for food.

Ant Colony Optimization technique has emerged recently novel meta-heuristic

for a hard combinational optimization problems. It is designed to stimulate the

ability of ant colonies to determine the shortest paths to food. Although

individual ant possess few capabilities, their operation as a colony is capable of

complex behavior. Real ants can indirectly can communicate though pheromone

information without visual cues and capable of finding shortest path between

food and their nests. The ants follow pheromone on trail while walking and the

other ants follow the trail with some probability dependent on the density of

pheromone deposited by the ants. The more the pheromone deposited the more

ants will follow that trail. Through this mechanism ants ultimately find the

shortest path.

Artificial ant can also imitate the behavior of real ants how they forage the food

but can also solve much more complicated problems than real ants can. A

search algorithm with such concept is called Ant Colony Optimization

Algorithm. ACO inspired by the forging behavior of real ants to find food from

their nest.

ALGORITHM:

Assuming S is source code and U= {U1, U2……...Um} donated a set of

destination nodes.

1. Initialize network nodes.

2. Set LC=0 where LC is loop count.

3. Let 𝐿𝑘 be the shortest path for the destination node Ui.

4. The initial value of 𝜏𝑘=0 as no ant has traversed any path so ant can

chose any path as probability of any path=0.

5. Ant chooses the path according to the probability of path.

6. Compute the pheromone update of the path and each edge selected by

the ant using

𝜏𝑘 =𝑅

𝐿𝑘

Where

R is any constant value.

Lk is total path traversed by the ant.

Update the local pheromone 𝜏𝑘

𝜏𝑘= (1-ρ) 𝜏𝑘+ 𝜏𝑘

Where ρ = (0 to 1) pheromone decay.

Compute the probability 𝑃𝑘 of each edge.

𝑃𝑘 =[𝜏]𝑘

𝛼 ∗ [𝑛]𝑘𝛽

∑ [𝜏]𝑘𝛼 ∗ [𝑛]𝑘

𝛽𝑗∈𝑛

𝑛𝑘= 1

𝑒𝑘

Where k ϵ N

α,β are meta values.

𝑛𝑘 heuristic value.

𝑒𝑘 edge value.

9. Set LC=LC+1.

Repeat from step 5 update the value of 𝜏𝑘 and probability of paths.

11. Collect best paths to get the multicast tree.

Solution and Results:

We consider the multicast routing problem with bandwidth and delay

constraints from one source node to muti-destination nodes.

Find the shortest path between 1 and 5 ?

Solution:-

α= 1 , β=1 , ρ=0.1 .

Initial τk = 0, Q = 10

𝑃𝑘 =[𝜏]𝑘

𝛼 ∗ [𝑛]𝑘𝛽

∑ [𝜏]𝑘𝛼 ∗ [𝑛]𝑘

𝛽𝑗∈𝑛

k ϵ N

Otherwise,

𝑛𝑘= 1

𝑑𝑘

𝜏𝑘 =𝑄

𝑐𝑘

τk = (1-ρ)τk + Δτk

Initially fk for every path = 0 as τk = 0

Ant chose path 1 2 4 5

C1,5 = d1,2 + d2,4 + d4,5

= 4+4+3 =11

Pheromone Value

Δτk =10/11

= 0.90

As τk = 0 + Δτk

τk = 0.90

2nd

Ant chose path

P1,2 =[(0.90)(1/4)/(0.9)(1/4)+0]=1

P2,4 = 1 , f4,5 = 1

P1,3 = 0 , f2,3 = 0, f3,5 = 0

Δτk = 0.90

τk = (1-0.1).90 + .90

= 1.71

3rd

Ant chose same path

ck = 11 , Δτk = .90

τk = (1-0.1)1.71 + .90

= 2.43

For path

τ1,2 = 2.43

τ2,4 = 2.43

τ4,5 = 2.43

4th

Ant chose path 1 2 3 5

Ck = d1,2 + d2,3 + d3,5

= 4 + 5 + 6 = 15

Δτk =10/15 =0.66 , τk = 0.66

τk 1,2 = (.9)*2.43 + 0.66 = 2.84

5th

Ant on same path

τk 2,3 = (.9)*0.66 + 0.66 = 1.25 = τ3,5

τ1,2 = (.9)*2.84 + 0.66 = 3.21

6th

Ant on same path

τk 2,3 = τ3,5 = (.9)*1.25 + 0.66 = 1.78

τ1,2 = (.9)*3.21 + 0.66 = 3.54

Path 1 3 5

ck = d1,3 + d3,5 = 5 + 6 = 11

Δτk =10/11= 0.9

7th

Ant

τ1,3 = .90

τ3,5 = (.9)*1.78 + .90

= 2.50

8th

Ant

τ1,3 = (.9)*(.90) + .90 = 1.71

τ3,5 = (.9)*2.50 + .90 = 3.15

9th

Ant

τ1,3 = (.9)*1.71 + .90 = 2.43

τ3,5 = (.9)*3.15 + .90 = 3.75

Destination Node The shortest paths

5

1 2 4 5

1 2 3 5

1 3 5

1 3 2 4 5

The route set from source to destination.

The above table shows all the possible paths to the destination node.

Hence,

τ1,2 = 3.54, τ1,3 = 2.43, τ2,3 = 1.78

τ2,4 = 2.43, τ3,5 = 3.75, τ4,5 = 2.43

Finding shortest path

Ant 10th

chose 1 2 4 5

ck = 11

Δ τk = .90

τ1,2 = (.9)*3.54 + .90 = 4.086

τ2,4 = (.9)*2.43 + .90 = 3.087

τ4,5 = (.9)*2.43 + .90 = 3.087

According to the above calculation path 1 2 4 5 has the highest probability.

Initially paths 1-2-4-5 and 1-2-3-5 were chosen by the ants. But the pheromones

on the path 1-2-4-5 were more, which lead to its higher probability to be chosen

by the ants.

Thus, 1-2-4-5 is the optimum path chosen by the ants. Given below is the

multicast tree obtained by the proposed Ant Algorithm.

CONCLUSION:

Hence by using the concept of ACO algorithm we found an optimum path to

send message from a source node to a destination node. The first ant moves

from the source to the destination leaving a trail of pheromones. The next ant

calculates the probability of the path based on the presence of pheromones on

that path. Higher the pheromone value on that path higher will be the

probability. And ant tends to choose higher probability path. Results show that

the proposed algorithm has features of well performances of cost, fast

convergence and stable delay.

NOTE:

1. I have some not about this paper that the researcher did make a

comparisons with other technique to prove what he deduced.

2. And also there is no explanation about who the Qos service improved by

using this technique and the old version that suffers from a serious

drawback i.e. the slow converbgence rate.