The Antnet Routing Algorithm - A Modified Version

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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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## The Antnet Routing Algorithm - A Modified Version

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### 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
• Background Information
• Ant Colony Optimisation
• Agent Based Routing Algorithms
• Antnet routing algorithm
• Improvements proposed
• Simulation Environment and Results
• Concluding Remarks
Aims & Objectives

Designing a routing algorithm:

• Scalable
• Distributed
• Intellegent
• Self - Organising
• Fault Tolerant
• Generic: Network and Machine Independent
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:

• 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
Classification
• Q-Learning
• Q-routing (Boyan et al, 94) (Tekiner et al., 04)
• Dual reinforcement Q-routing(Kumar et al., 97 & 01)
• Ant (software agent) based Routing Algorithms
• 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)
Comparison of Algorithms
• 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)
Ants In Nature -“unsophisticated and simple”
• 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
Ants – How do they Find Their Way?
• 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.
Antnet in Detail

Positive reinforcement:

Negative reinforcement:

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

Simulation Parameters
• 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
Results 1

Ant rate vs. avg. delay

Results 2

Ant rate vs. the throughput

Concluding Remarks
• 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
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).
Acknowledgement

Thanks to my sponsor

Northumbria University

Any Questions?