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 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 “unsophisticated and simple” 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? i.Ants don’t know where to go initially, and choose paths randomly ii.Ants taking the “shorter path” will reach the destinations before the those taking a long route. The path is marked with pheromone. iii.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 optimise There is a need for a second heruistic to optimise antnet’s parameters Stagnation 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. ~7%  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?