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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.
Date of Conference: 6-10 Dec. 2010