Inductive Routing System Based on QoS Bandwidth Optimization

Computing constrained shortest paths is fundamental to some important network functions such as QoS routing and traffic engineering. This paper introduces a polynomial time approximation Quality of Service (QoS) routing algorithm and constructs dynamic state-dependent routing policies. The proposed algorithm uses a bio-inspired approach based on the trial/error paradigm combined with swarm adaptive approaches to optimize three QoS different criteria: static cumulative cost path, dynamic residual bandwidth, and end-to-end delay. The approach uses a model that combines both a stochastic planned pre-navigation for the exploration phase and a deterministic approach for the backward phase. In this paper, we adopt the unified framework of online learning to consider a global cost function. Numerical results obtained with OPNET simulator for different levels of traffic's load show that the new added module improves clearly performances of our earlier KOQRA.

[1]  Sartaj Sahni,et al.  Data Structures, Algorithms, and Applications in C++ , 1997 .

[2]  Falko Dressler,et al.  Bio-Inspired Networking - Self-Organizing Networked Embedded Systems , 2008, Organic Computing.

[3]  David Eppstein,et al.  Finding the k shortest paths , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[4]  Michael L. Littman,et al.  Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach , 1993, NIPS.

[5]  Manish Jain,et al.  Pathload: A Measurement Tool for End-to-End Available Bandwidth , 2002 .

[6]  Marco Dorigo.  Ant colony optimization , 2004, Scholarpedia.

[7]  Said Hoceini,et al.  Reinforcing Probabilistic Selective Quality of service Routes in Dynamic Heterogeneous Networks , 2008 .

[8]  Franco Zambonelli,et al.  A survey of autonomic communications , 2006, TAAS.

[9]  Piet Van Mieghem,et al.  Conditions that impact the complexity of QoS routing , 2005, IEEE/ACM Transactions on Networking.

[10]  Falko Dressler,et al.  Self-Organized Network Security Facilities based on Bio-inspired Promoters and Inhibitors , 2007, Advances in Biologically Inspired Information Systems.

[11]  Said Hoceini,et al.  K-Shortest Paths Q-Routing: A New QoS Routing Algorithm in Telecommunication Networks , 2005, ICN.

[12]  Michael Randolph Garey,et al.  Johnson: "computers and intractability , 1979 .

[13]  Said Hoceini,et al.  Adaptive quality of service-based routing approaches: development of neuro-dynamic state-dependent reinforcement learning algorithms , 2007, Int. J. Commun. Syst..

[14]  M. I. Henig.  Vector-Valued Dynamic Programming , 1983 .

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

[16]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[17]  Richard G. Baraniuk,et al.  pathChirp: Efficient available bandwidth estimation for network paths , 2003 .

[18]  Horst F. Wedde,et al.  BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior , 2005, GECCO '05.

[19]  Léon Bottou,et al.  Stochastic Learning , 2003, Advanced Lectures on Machine Learning.

[20]  Andy M. Tyrrell,et al.  BIOLOGICALLY INSPIRED FAULT-TOLERANT ARCHITECTURES FOR REAL-TIME CONTROL APPLICATIONS , 1999 .

[21]  Luca Maria Gambardella,et al.  AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks , 2005, Eur. Trans. Telecommun..

[22]  Said Hoceini,et al.  Reinforcing probabilistic selective Quality of Service routes in dynamic irregular networks , 2008, Comput. Commun..

[23]  Azzedine Boukerche,et al.  Quality of service based routing algorithms for heterogeneous networks [Guest editorial] , 2007, IEEE Communications Magazine.

[24]  Risto Miikkulainen,et al.  On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning , 1996, ICML.

[25]  Erol Gelenbe,et al.  Power-aware ad hoc cognitive packet networks , 2004, Ad Hoc Networks.