Distributed and Scalable Path Management by a System of Cooperating Ants

Path management in next generation networks will be computationally excessive if guarantied quality of service is to be offered. Route computations subject to many and changing requirements set forth by a range of applications is far from straight forward when applying state-of-the-art routing systems. Robust and adaptive swarm based systems are candidates to handle future path management challenges. One such system, the Cross Entropy Ants System (CEAS), provides stochastic, asynchronous and truly distributed path management. In most management systems there is a trade-off between performance and management overhead. This paper presents an improved version of CEAS, denoted Subpath CEAS. Significant savings are observed in memory usage, and in the number of control packets generated, without loss of performance or added processing. To achieve this, end-toend paths with the same destination and QoS requirements are made to share and exploit information about their common sub-paths.

[1]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  Bjarne E. Helvik,et al.  Using the Cross-Entropy Method to Guide/Govern Mobile Agent's Path Finding in Networks , 2001, MATA.

[5]  R. Rubinstein The Cross-Entropy Method for Combinatorial and Continuous Optimization , 1999 .

[6]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[7]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[8]  Ross W. Callon,et al.  Use of OSI IS-IS for routing in TCP/IP and dual environments , 1990, RFC.

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  R. Bellman Dynamic programming. , 1957, Science.

[11]  Poul E. Heegaard,et al.  Self-tuned Refresh Rate in a Swarm Intelligence Path Management System , 2006, IWSOS/EuroNGI.

[12]  Gang Wang,et al.  A Distributed Ant Colony Algorithm Based on Cross-Entropy for Multi-Constraints QoS Routing , 2007, The 9th International Conference on Advanced Communication Technology.

[13]  HeidelbergerPhilip Fast simulation of rare events in queueing and reliability models , 1995 .

[14]  Deep Medhi,et al.  Routing, flow, and capacity design in communication and computer networks , 2004 .

[15]  Léon J. M. Rothkrantz,et al.  Ant-Based Load Balancing in Telecommunications Networks , 1996, Adapt. Behav..