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

doi:10.1016/S1005-8885(10)60106-9

Acta Metallurgica Sinica(English letters) ›› 2011, Vol. 18 ›› Issue (5): 71-75.doi: 10.1016/S1005-8885(10)60106-9

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

陈亮

北京邮电大学

收稿日期:2011-01-10 修回日期:2011-06-02 出版日期:2011-10-31 发布日期:2011-10-13
通讯作者: 陈亮 E-mail:keanu0579@sohu.com
基金资助:
This work was supported by the Ministry of Industry and Information Technology of China (2011ZX03001-007-03), the Nature Science Foundation of Beijing (4102044), and the National Science Foundation for Young Scientists of China (61001115).

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

College of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2011-01-10 Revised:2011-06-02 Online:2011-10-31 Published:2011-10-13
Contact: CHEN Liang E-mail:keanu0579@sohu.com
Supported by:
This work was supported by the Ministry of Industry and Information Technology of China (2011ZX03001-007-03), the Nature Science Foundation of Beijing (4102044), and the National Science Foundation for Young Scientists of China (61001115).

摘要/Abstract

摘要：

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.

关键词:

hopfield neural network, CPN, multi-path routing

Abstract:

Key words:

hopfield neural network, CPN, multi-path routing

中图分类号:

TN919

参考文献

1. Li Y L, Chen W B. An efficient tile-based ECO router using routing graph reduction and enhanced global routing flow. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2007, 26(2): 345-358

2. Gelenbe E, Lent R, Montuori A. Cognitive packet networks: QoS and performance. Proceedings of the 10th IEEE/ ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems (MASCOTS’02), Oct 11-16, 2002, Fort Worth, TX, USA. Piscataway, NJ, USA: IEEE, 2002: 3-9

3. Gelenbe E, Lent R, Xu Z G. Design and performance of cognitive packet networks. Performance Evaluation, 2001, 46(2/3): 155-176

4. Gelenbe E, Gellman M. Can routing oscillations be good? the benefits of route-switching in self-aware networks. Proceedings of the 15th IEEE /ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems (MASCOTS’07), Oct 24-26, 2007, Istanbul, Turkey. Piscataway, NJ, USA: IEEE, 2007: 343-352

5. Rauch H E, Winarske T. Neural networks for routing communication traffic. IEEE Control Systems Magazine, 2002, 8(2): 26-31

6. Ali M K M, Kamoun F. Neural networks for shortest path computation and routing in computer networks. IEEE Transactions on Neural Networks, 1993, 4(6): 941-953

7. Bastos-Filho C J A, Santana R A, Oliveira A L I. A novel approach for a routing algorithm based on a discrete time hopfield neural network. Proceedings of the IEEE Symposium on Foundations of Computational Intelligence (FOCI’07), Apr 1-5, 2007, Honolulu, HI, USA. Piscataway, NJ, USA: IEEE, 2007: 363-369

8. Schuler W H, Bastos-Filho C J A, Oliveira A L I. A novel hybrid training method for hopfield neural networks applied to routing in communications networks. Proceedings of the 7th International Conference on Hybrid Intelligent Systems (HIS’07), Sep 17-19, 2007, Kaiserslautern, Germany. Washington, DC, USA: IEEE Computer Society, 2007: 36-41

9. Shaikh-Husin N, Hani M K, Seng T G. Implementation of recurrent neural network algorithm for shortest path calculation in network routing. Proceedings of the 2002 International Symposium on Parallel Architectures, Algorithms and Networks (ISPAN’02), May 22-24, 2002, Manila, Philippines. Washington, DC, USA : IEEE Computer Society, 2002: 91-96

10. Lin C, Liao F Y, Tsao C K, et al. A hierarchical multiple-view approach to three-dimensional object recognition. IEEE Transactions on Neural Networks, 1991, 2(1): 84-92

11. Li M G, Ruan X G. Dynamic tracking optimization by continuous Hopfield neural network. Proceedings of the 5th World Congress on Intelligent Control and Automation (WCICA’04): Vol 3, Jun 15-19, 2004, Hangzhou, China. Piscataway, NJ, USA: IEEE, 2004: 2598-2602

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

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

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