Multi-path routing based on load-balance for cognitive packet networks

Abstract Quality of service (QoS) routing algorithms have been hardly discussed in the scientific community, most previous work on QoS routing concentrates on the performance of the single route. Cognitive packet network (CPN) has been designed for providing QoS routing. In this paper, to balance the loads among networks, we present a multi-path routing algorithm based on load-balance (MPRLB), which is carried out in two steps. The algorithm with low computational complexity is firstly applied to establish multi path routing for each source and destination node pairs (SD-pair) nodes in the network. Then, we propose the hopfield neural network algorithm, which is applied to improve the efficiency of the flow deviation method for fast flow allocation among the links of the network based on load balance. Extensive simulation results demonstrate that the proposed scheme significantly improves the performance compared with the existing scheme that ignores load balancing.

[1]  H.E. Rauch,et al.  Neural networks for routing communication traffic , 1988, IEEE Control Systems Magazine.

[2]  Faouzi Kamoun,et al.  Neural networks for shortest path computation and routing in computer networks , 1993, IEEE Trans. Neural Networks.

[3]  Yih-Lang Li,et al.  An Efficient Tile-Based ECO Router Using Routing Graph Reduction and Enhanced Global Routing Flow , 2007, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[4]  Adriano Lorena Inácio de Oliveira,et al.  A Novel Approach for a Routing Algorithm Based on a Discrete Time Hopfield Neural Network , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[5]  Mohamed Khalil Hani,et al.  Implementation of recurrent neural network algorithm for shortest path calculation in network routing , 2002, Proceedings International Symposium on Parallel Architectures, Algorithms and Networks. I-SPAN'02.

[6]  Wei-Chung Lin,et al.  A hierarchical multiple-view approach to three-dimensional object recognition , 1991, IEEE Trans. Neural Networks.

[7]  Erol Gelenbe,et al.  Cognitive packet networks: QoS and performance , 2002, Proceedings. 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems.

[8]  Erol Gelenbe,et al.  Design and performance of cognitive packet networks , 2001, Perform. Evaluation.

[9]  Erol Gelenbe,et al.  Can Routing Oscillations be Good? The Benefits of Route-switching in Self-aware Networks , 2007, 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.

[10]  Adriano Lorena Inácio de Oliveira,et al.  A Novel Hybrid Training Method for Hopfield Neural Networks Applied to Routing in Communications Networks , 2007, HIS.

[11]  Ruan Xiaogang,et al.  Dynamic tracking optimization by continuous Hopfield neural network , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).