The Antnet Routing Algorithm - A Modified Version

1. 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

2. Contents • Background Information • Ant Colony Optimisation • Agent Based Routing Algorithms • Antnet routing algorithm • Improvements proposed • Simulation Environment and Results • Concluding Remarks

3. Aims & Objectives Designing a routing algorithm: • Scalable • Distributed • Intellegent • Self - Organising • Fault Tolerant • Generic: Network and Machine Independent

4. 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

5. 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)

6. 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)

7. 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

8. 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.

9. Antnet in Detail Positive reinforcement: Negative reinforcement:

10. 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

11. Simulation Network

12. 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

13. Results 1 Ant rate vs. avg. delay

14. Results 2 Ant rate vs. the throughput

15. 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

16. 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).

17. Acknowledgement Thanks to my sponsor Northumbria University Any Questions?