The Antnet Routing
Algorithm - A Modified
Version
Firat Tekiner, Z. Ghassemlooy
Optical Communications Research Group, The
University of Northumbria, Newcastle upon Tyne
S. Alkhayatt
School of Computing Science, Sheffield Hallam
University
CSNDSP 20-22 July 2004
Contents
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Background Information
Ant Colony Optimisation
Agent Based Routing Algorithms
Antnet routing algorithm
Improvements proposed
Simulation Environment and Results
Concluding Remarks
2
Aims & Objectives
Designing a routing algorithm:
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Scalable
Distributed
Intellegent
Self - Organising
Fault Tolerant
Generic: Network and Machine
Independent
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Routing
“In internetworking, the process of moving a packet of
data from source to destination.”
A routing algorithm is necessary to find the optimal path
(or the shortest path) from source to destination.
Problems:
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Existing algorithms are mostly Table-Based (high cost)
Congestion and contention (requires traffic distribution)
Requires human intelligence
The routing algorithms that are in use are all static
algorithms
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Classification
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Q-Learning
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Ant (software agent) based Routing Algorithms
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Q-routing (Boyan et al, 94) (Tekiner et al., 04)
Dual reinforcement Q-routing (Kumar et al., 97 & 01)
ABC routing (Schoonderwoerd et al., 96)
Regular and Uniform ant routing (Subramanian et al., 97)
Antnet (Dorigo et al., 98)
Antnet++ (Dorigo et al., 02)
Improved Antnet (Boyan et al., 02)
Antnet with evaporation (Tekiner et al. 2, 04)
Agent Distance Vector Routing (ADVR)
(Amin et al., 01
& 02)
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Comparison of Algorithms
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Antnet uses probabilistic routing tables whereas in
Link State and Distance Vector routing table entries
are deterministic
Ants use less resources on the nodes
Ants are dynamic and self organising whereas
Distance Vector and Link State algorithms require
human supervision
Q-Routing does not guarantee on finding the
shortest path always. Moreover, they can only find a
single path, they cannot explore multiple paths
In antnet stagnation is the main problem (routing
table freezes due to selecting same path)
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Ants In Nature -
“unsophisticated and simple”
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Builds and protects their nests
Sorts brood and food items
Explore particular areas for food, and preferentially
exploits the richest available food source
Cooperates in carrying large items
Migrates as colonies
Leaves pheromones on their way back
Stores information in the nature (uses world as a
memory)
Make decision in a stochastic way
Always finds the shortest paths to their nests or food
source
Are blind, can not foresee future, and has very limited
memory
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Ants – How do they Find
Their Way?
i.
ii.
iii.
Ants don’t know where to go initially, and choose paths randomly
Ants taking the “shorter path” will reach the destinations before
the those taking a long route. The path is marked with
pheromone.
There after the number of ants using the shorter path will keep
increasing, since more pheromone is laid on the path.
(i)
(ii)
(iii)
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Antnet in Detail
Search for
output port
Data Packet Queue
Antnet
Routing algorithm
P11
P12
…..
P21
P1N
P2N
Search result
High Priority Ant Queue
PL-1 N
PL1
...
PL N-1
Outgoing Links (L)
Network Nodes (N)
PLN
Update probability entry
based on r
Push the current
time to ants stack
Network Nodes (N)
Push observed trip time to
the array
Positive reinforcement:
Negative reinforcement:
Statis
tic(1)
Statis
tic(2)
……
….
Statis
tic(N)
Pfd   Pfd   r (1  Pfd  )
Pnd   Pnd   rPnd  , nN k , n  f
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Three Improvements
A. Deleting aged packets
if PACKET AGE > 2 x NO_OF_NODES
then DROP PACKET
B. Limiting the effect of r
if (NO_OF_NODES <= 5)
0.1 < r < (1 – 0.1 * NO_OF_NODES)
else /* if (NO_OF_NODES > 5) */
0.05 < r < (1– 0.05 * NO_OF_NODES)
C. Limiting the number of Ants in the system
NO OF ANTS CREATED
 0.001
NO OF PACKETS SEND
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Simulation Network
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Simulation Parameters
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Poisson traffic distribution, with three different
system loads low, medium and high
5000 packets created per node
Average of 8 simulation runs is used for
accuracy
No packet loss due to node/link failures
All experiments are implemented for varying
ant creation rates, since it has a significant
effect on the performance of the algorithm
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Results 1
Ant rate vs. avg. delay
1.150
Avg. Packet Delay(s)
1.100
1.050
Mid-Mod
1.000
Mid-Orj
0.950
0.900
0.850
1.000
0.100
0.010
0.001
Ant Rate
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Results 2
Ant rate vs. the throughput
1.05
1.00
Packet / Time (ms)
0.95
LowMod
Low-Orj
0.90
0.85
0.80
MidMod
Mid-Orj
0.75
0.70
HighMod
HighOrj
0.65
0.60
1.000
0.100
0.010
0.001
Ant Rate
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Concluding Remarks
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Detecting and removing aged packets
improved networks performance
Boundaries introduce reduces the effect of
the traffic fluctuations on the solution
No mathematical formula only constant
variables are used
There is a need for a second heruistic to
optimise antnet’s parameters
Stagnation is a major problem but solution
does exists
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Current and Future Work
 Current Work:
Stagnation problem is currently being investigated in different
traffic models and network configurations.
 Evaporation: ~7% improvement in the performance of
the algorithm [Tekiner et al. 2, SoftCOM04]
 Multiple Ant Colonies
 Aging, and Noise
 Future Work:
 Hybrid Algorithm: Distributed GA could be embedded in the
proposed model [Tekiner et al., seminar 2]
 Together with hybrid GA all constant variables used needs to be
dynamic (currently static variables used).
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Acknowledgement
Thanks to my sponsor
Northumbria University
Any Questions?
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