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.