Discrete event modeling of swarm intelligence based routing in network systems

Simulation remains attractive for performance and scalability analysis and/or design of networks. This paper presents a biologically inspired discrete-event modeling approach for simulating alternative computer network protocols. This approach identifies and incorporates the key attributes of honeybees and their societal properties into simulation models that are formalized according to the Discrete Event System Specification (DEVS) formalism. We describe our approach with particular emphasis on how to model the individual honeybees and their cooperation. These models, collectively referred to as SwarmNet, support routing algorithms akin to honeybees searching for and foraging on food. Adaptation and probabilistic specifications are introduced into honeybee (BEE) and Routing Information Protocol (RIP) routing algorithms. A set of simulation experiments are developed to show the biologically inspired network modeling with the BEE routing algorithm, as compared with the RIP routing algorithm, offers favorable throughput and delay performance and also exhibit superior survivability against network load surges. The paper concludes with some observations on the SwarmNet modeling approach and outlines some future research directions.

[1]  M.A. El-Sharkawi,et al.  Swarm intelligence for routing in communication networks , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[2]  Kai-Yuan Cai,et al.  Software execution processes as an evolving complex network , 2009, Inf. Sci..

[3]  Marco Dorigo,et al.  From Natural to Artificial Swarm Intelligence , 1999 .

[4]  Peng Shi,et al.  Using investment satisfaction capability index based particle swarm optimization to construct a stock portfolio , 2011, Inf. Sci..

[5]  Devika Subramanian,et al.  Ants and Reinforcement Learning: A Case Study in Routing in Dynamic Networks , 1997, IJCAI.

[6]  Marco Dorigo,et al.  Ant Colonies for Adaptive Routing in Packet-Switched Communications Networks , 1998, PPSN.

[7]  Martin Heusse,et al.  Adaptive Agent-Driven Routing and Load Balancing in Communication Networks , 1998, Adv. Complex Syst..

[8]  Bernard P. Zeigler,et al.  Theory of modeling and simulation , 1976 .

[9]  S. Appleby,et al.  Mobile Software Agents for Control in Telecommunications Networks , 2000 .

[10]  Horst F. Wedde,et al.  A comprehensive review of nature inspired routing algorithms for fixed telecommunication networks , 2006, J. Syst. Archit..

[11]  Bernard P. Zeigler,et al.  Exploiting HLA and DEVS To Promote Interoperability and Reuse in Lockheed's Corporate Environment , 1999, Simul..

[12]  Mehmet Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem , 2011, Inf. Sci..

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

[14]  Jing Wang,et al.  Swarm Intelligence in Cellular Robotic Systems , 1993 .

[15]  Ahmet Zengin,et al.  Modeling discrete event scalable network systems , 2011, Inf. Sci..

[16]  Carl Anderson,et al.  The adaptive value of inactive foragers and the scout-recruit system in honey bee (Apis mellifera) colonies , 2001 .

[17]  Bernard P. Zeigler,et al.  Parallel DEVS: a parallel, hierarchical, modular modeling formalism , 1994, Proceedings of Winter Simulation Conference.

[18]  Feng Lin,et al.  Decision making in fuzzy discrete event systems , 2007, Inf. Sci..

[19]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[20]  Muddassar Farooq,et al.  From the wisdom of the hive to intelligent routing in telecommunication networks: a step towards intelligent network management through natural engineering , 2006 .

[21]  Bijaya K. Panigrahi,et al.  Multi-objective optimization with artificial weed colonies , 2011, Inf. Sci..

[22]  Ahmet Zengin,et al.  Large-Scale Integrated Network System Simulation with DEVS-Suite , 2010, KSII Trans. Internet Inf. Syst..

[23]  Bernard P. Zeigler,et al.  Rtdevs/corba: a distributed object computing environment for simulation-based design of real-time discrete event systems , 2001 .

[24]  Bernard P. Zeigler,et al.  Design and implementation of distributed real-time DEVS/CORBA , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[25]  Bernard P. Zeigler,et al.  DEVS/SOA: A Cross-Platform Framework for Net-centric Modeling and Simulation in DEVS Unified Process , 2009, Simul..

[26]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[27]  M. Steenstrup Routing in communications networks , 1995 .

[28]  Muddassar Farooq Bee-Inspired Protocol Engineering: From Nature to Networks , 2008 .

[29]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .