The document describes an optimization technique for the AntNet routing algorithm. AntNet is an ant colony optimization inspired routing algorithm where artificial ants called forward and backward agents explore network paths and update routing tables. The optimization works by sorting values in routing tables, comparing differences, and selecting multiple optimal outgoing paths if their values are close, to prevent stagnation and allow exploration of alternative paths. This improves network throughput by identifying multiple optimal paths and resolving the stagnation problem in AntNet routing.

1. OPTIMIZATION IN ANTNET ROUTING
ALGORITHM
Vinay
Kumar

2. BACKGROUND
• What is Routing?
 Routing is the process of selecting paths in a network to send data packets.
 It fully affects the throughput, reliability, and congestion of the network.
• What is an ideal routing algorithm?
 An algorithm which can deliver a packet from source to its destination with
min. amount of delay.
 It must be adaptive and intelligent to make decisions according to
circumstances.

3. BACKGROUND…
• Routing algorithms
 OSPF, RIP, BGP are some routing algorithms in use.
 Not sufficient to tackle the complexity of networks.
 Not adaptive, not intelligent and fault intolerant.

4. INTRODUCTION
• Ant is a common insect found in your home.
• Its foraging behavior
 Discovers shortest path to a food source.
 Shares the info to other ants by stigmergy.
 Stigmergy is a form of indirect communication used by ant for problem solving activity.
 It drops pheromone through the way it goes. It follows the backtrack path with maximum
pheromones. That path is actually the shortest path.

5. INTRODUCTION…
• Ant Colony Optimization [ACO] – A family of optimization algorithms based on real ant
behavior.
• AntNet Routing Algorithm
 A routing algorithm based on ACO
 A group of artificial ants (agents) create the shortest path between a pair of nodes.
 They update the routing tables.
 Data packets are directed towards destination using the updated routing table.

6. DATA STRUCTURE MAINTAINED AT EACH NODE
1. Routing table [Tk] –
 It contains the information specifying which is the next node should be taken in order to
reach a destination.
 Row represents all adjacent nodes to current node.
 Column represents all possible destinations in the network.
 Table entry is represented by Pnd ,
Where Pnd = goodness of choosing n as next node in order to reach destination d
OR
Pheromone concentration along the link from current node to next node
n for destination d

7. DATA STRUCTURE MAINTAINED AT EACH NODE
2. Local traffic statistics
• It follows the traffic fluctuations as seen by current node
• Stored as an array where each entry contains,
 µd=estimated mean
 σ2=estimated variance
 Wd=moving observation window
for a destination node d.

8. ANTNET ROUTING ALGORITHM
• The operation involves two types of agents-
 Forward agents [Fs->d] who gather information about the state of network.
 Backward agents [Bd->s] who use the collected information from Fs->d to update the routing
tables of routers on their path.

9. ANTNET ROUTING ALGORITHM
• Fs->d is launched towards destination d to discover a shortest and least-cost path at
regular time intervals.
• Fs->d maintain a stack which stores –
 Identifier of every visited node i
 Time elapsed in arriving at node i from originating node
At any node i, Fs->d selects the next adjacent node j by using the probability value,

10. ANTNET ROUTING ALGORITHM
• If Fs->d reaches an already visited node(cycle) then the information of this node is popped
from the stack.
• If cycle is greater than half the Fs->d lifetime then the Fs->d is destroyed.
• When Fs->d reaches the destination node d then a backward agent Bd->s is created.
• Fs->d transfers its stack information to Bd->s and dies.
• Bd->s starts from d towards s following the same path as Fs->d by popping the stack at each
node.
• As Bd->s reaches a node ,it updates both the data structure of the node.

11. UPDATING LOCAL TRAFFIC STATISTICS
• The estimated mean and variance are updated as follows-
 id   id   ( o i  d   id )
 id   id   (( o i  d   id )   id )
2 2 2 2
Where, Oi->d is the observed ant’s trip time from node i to destination d
η weighs the number of most recent samples that will really affect average

12. UPDATING ROUTING TABLE
• If Bd->s arrives at a node i from a node f ,then the entry in the routing table is updated by
the formula-
Pifd  Pifd  r (1  Pifd )
Where r is reinforcement value depending on tables of node i, Ti and Mi
• Simultaneously, the pheromone value for nodes other than f is decreased so that total
sum of probability still comes 1.
Pind  Pind  rPind , n  f
• As soon as Bd->s finishes, the data packet can be sent into the network according to
updates routing tables..

13. LIMITATIONS
• Stagnation is the major problem in antnet algorithm. If a link z keeps good condition for a
long time, then its probability value will be high thus leading to more and more packets to
be sent via link z. It reduces the probability of other links.
• Stagnation is very critical problem because-
 Link z may lose its optimality.
 If network gets failed, then link z may become unavailable.
 Other long paths may become optimal which otherwise should not.

14. OPTIMIZATION
Optimization operation is performed on Routing table -
• The column wise values in the table are picked up and a sorting algorithm is executed on
these values.
• The sorted values ranging from higher to lower are stored in a temporary array.
• The difference d, amongst the adjacent values is calculated and is compared to some
threshold value say pm.
• If the difference d is less than pm then those values are selected and comparison amongst
the adjacent values is continued until difference is greater than pm.
• Otherwise at the very first occurrence of difference greater than pm , the comparison is
stopped and the corresponding values in the array are selected.
• The interfaces corresponding to these values are stored in a new routing table which will
have the same structure as the original one, but obviously, the new table will have less
number of rows.

15. WORKING

16. WORKING…
Destination nodes
E G F A
A .25 .39 .8 .34
Next nodes
C .45 .32 0 .34
D .17 .11 .1 .20
F .13 .28 .1 .12
Ist column Assumption: pm =0.1 & Node B Routing table shown
.45 .25 .17 .13
{.45} is selected

17. WORKING
Destination nodes
E G F A
A .25 .39 .8 .34
Next nodes
C .45 .32 0 .34
D .17 .11 .1 .20
F .13 .28 .1 .12
IInd column
.39 .32 .28 .11
{.39,.32,.28} is selected

18. WORKING
Destination nodes
E G F A
A .25 .39 .8 .34
Next nodes
C .45 .32 0 .34
D .17 .11 .1 .20
F .13 .28 .1 .12
IIIrd column
.8 .1 .1 0
{.8} is selected

19. WORKING
Destination nodes
E G F A
A .25 .39 .8 .34
Next nodes
C .45 .32 0 .34
D .17 .11 .1 .20
F .13 .28 .1 .12
IVth column
.34 .34 .20 0.12
{.34,.34} is selected

20. WORKING
E G F A
Next nodes
A .39 .8 .34
C .45 .32 .34
F .28
Result- Thus, more than one optimal paths if exist are identified.

21. CONCLUSIONS
• We find more than one optimal outgoing path.
• It can explore new and better paths even if network topologies change frequently.
• The problem of stagnation is resolved.
• The throughput of network is improved.

22. REFERENCES
[1] Shuchita Upadhyaya and Richa Setiya , “Identifying multiple optimal paths in Antnet Routing
Algorithm with negligible overhead,” IJCSNS International Journal of Computer
Science and Network Security, VOL.9 No.2, February 2009
[2] F. Tekiner, F. Z. Ghassemlooy and S. Al-khayatt, “The Antnet Routing Algorithm – A
Modified Version”
[3] soe.northumbria.ac.uk
Thanks …. Any Questions?