Congestion, packet loss and increased response-time due to network traffic are common problems in most networks. This results in lowered network efficiency and poor Quality of Service (QoS). A number of routing protocols have been developed to deal with network traffic. The goal of every network routing protocol is to direct the traffic from source to destination maximizing the network performance. The Ant Colony Optimization (ACO) based routing protocol is efficient when used to dynamically route network traffic. Currently, there are many variations of the ACO algorithm in the domain of network routing. Past work has been done by researchers to improve the performance of the algorithm. In this paper we first study and analyze the existing work in this field and weigh the pros and cons of the different modifications and variations of the algorithm. We then propose a modification to the ACO algorithm in order to improve the quality of service offered by a network by routing packets according to their priority. Packets that belong to time-sensitive services like VOIP will be given higher priority and routed differently from low priority packets like FTP. By doing so the proposed algorithm will improve the success rate of the high priority packets while still maintaining high overall throughput of the network by dropping low priority packets that form loops. We then implement this algorithm on NS2 network simulator. The algorithm is then tested to see how it dynamically adapts to network changes. We then conduct tests to calculate the success rate and throughput that is offered by the algorithm and compare the results to those of other ACO algorithms. Our results indicate that the proposed algorithm improves the overall performance of the network by striking a balance between throughput and success rate thanks to the priority mechanism.

Computer Networks; Routing; QoS; Ant Colony Optimization; Swarm Intelligence

[1] Debasmita Mukherjee and Sriyankar Acharyya, “Ant Colony Optimization Technique Applied in Network Routing Problem”, International Journal of Computer Applications, Volume-01, Issue-15, Page no (66-73), May 2012
[2] Chris Saliba and Reuben A. Farrugia, “Quality of Service Aware Ant Colony Optimization Routing Algorithm”, 15th IEEE Mediterranean Electrotechnical Conference, ISBN: 978-1-4244-5793-9, Page no (343-347) , April 26-28, 2010.
[3] Masaya Yoshikawa and Kazuo Otani, “Ant Colony Optimization Routing Algorithm with Tabu Search”, Proceedings of the International Mulitconference of Engineers and Computer Scientists 2010, Volume – III, ISBN: 978-988-18210-5-8, Page no (112-117) March 17-19, 2010.
[4] Vincent Verstraete, Matthias Strobbe, Erik Van Breusegem, Jan Coppens, Mario Pickavet and Piet Demeester, “AntNet: ACO routing algorithm in practice”, Proceedings of the 8e INFORMS Telecommunications Conference, 2006.
[5] Gianni Di Caro and Marco Dorigo, “Ant colonies for Adaptive Routing in Packet-Switched Communications Networks”, Lecture Notes in Computer Science, Volume 1498, Page no (673-682), June 2006.
[6] The Network Simulator – NS2, http://www.isi.edu/nsnam/ns/, August 2014.
[7] V. Laxmi, Lavina Jain and M.S. Gaur, "Ant Colony Optimization Based Routing on NS-2", International Conference on Wireless Communication and Sensor Networks (WCSN), India, December 2006.
[8] Gianni Di Caro and Marco Dorigo, “AntNet : Distributed Stigmergetic Control For Communications Network”, Journal of Artificial Intelligence Research, Volume-09, Issue-01, ISSN 1076–9757, Page no (317-365), August 1998.