Research on a New Network Model for Fault Location Based on Betweenness

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 117)

Abstract

As the structure of modern computer networks has become more and more complex, the SNMP[1] protocol’s fault management modes are challenged in the traditional network management. Higher requests have been put forward to the fault location research in network management about how to quickly find a network fault point in the complex network structure and to improve the efficiency of network management. In recent years, studies on complex networks[2] are still growing, so it is a direction worthy of research and concerns on how to combine the network management with the information contents which describe the relevant natures and dimensions of networks in complex networks, to enhance functions of the network management model, and to fast capture the fault point. In this paper, through simulation, it has been discovered that there is a certain percentage between a node’s betweenness and its patency. That is, when a fault occurs, the higher a node’s betweenness, the greater the possibility of a fault’s occurring on another node which is on the shortest path passing the former node. Therefore, through the introduction of a node’s betweenness and other related factors in complex networks into the algorithm for discovering a fault point in network management, a new fault location algorithm is put forward. Also, based on experimental simulations and algorithm performance tests in real situations, it enhances the effects of polling and Trap traditional mechanisms in the previous SNMP protocol, effectively reduces the fault detection time, and improves the location accuracy.

Keywords

Network management Complex network Betweenness fault detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Subramanian, M.: Network Management——Principles and Practice (Photocopy Edition), pp. 562–563. Higher Education Press, Beijing (2002)Google Scholar
  2. 2.
    Barabasi, A.-L., Bonabeau, E.: Scale-free networks. Scientific American 288, 60–69 (2003)CrossRefGoogle Scholar
  3. 3.
    Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006)CrossRefGoogle Scholar
  4. 4.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small world ’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  5. 5.
    Nanavati, A.A., Singh, R., et al.: Analyzing the structure and evolution of massive telecom graphs. IEEE Transactions on Knowledge and Data Engineering 20(5), 703–718 (2008)CrossRefGoogle Scholar
  6. 6.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. of the National Academy of Science 9(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Lo, C.-C., Chen, S.-H., Lin, B.-Y.: Coding-based schemes for fault identification in communication networks. International Journal of Network Management 10(3), 157–164 (2000)CrossRefGoogle Scholar
  8. 8.
    Hasan, M., Sugla, B., Viswanathan, R.: A conceptual framework for network management event correlation and filtering systems. In: Proceedings of the Sixth IFIP/IEEE International Symposium on Integrated Network Management (IM), Boston, pp. 233–246 (1999)Google Scholar
  9. 9.
    Hood, C., Ji, C.: Proactive network fault detection. IEEE Transactions on Reliability 46(3), 333–341 (1997)CrossRefGoogle Scholar
  10. 10.
    Aghasaryan, A., Fabre, E., Benveniste, A., Boubour, R., Jard, C.: A Petri net approach to fault detection and diagnosis in distributed systems. In: 36th IEEE Conference on Decision and Control (CDC), IEEE Control Systems Society, San Diego, pp. 726–731 (1997)Google Scholar
  11. 11.
    Boubour, R., Jard, C.: Fault detection in telecommunication networks based Petric net representation of alarm propagation. In: Azéma, P., Balbo, G. (eds.) ICATPN 1997. LNCS, vol. 1248, pp. 367–386. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  12. 12.
    Bouloutas, A., Calo, S., Finkel, A.: Alarm correlation and fault identification in communication networks. IEEE Transactions on Communications 42(2), 523–533 (1994)CrossRefGoogle Scholar
  13. 13.
    Chao, C.S., Yang, D.L., Liu, A.C.: An automated fault diagnosis system using hierarchical reasoning and alarm correlation. Journal of Network and Systems Management 9(2), 183–202 (2001)CrossRefGoogle Scholar
  14. 14.
    Mohamed, E.A., Rao, N.D.: Artificial neural network based fault diagnostic system for electric power distribution feeders. Electric Power System Research 35, 1–10 (1995)CrossRefGoogle Scholar
  15. 15.
    Li, H., Baras, J.S., Mykoniatis, G.: An Automated, Distributed, Intelligent Fault Management System for Communication Networks, Technical Report, TR 99-57, University of Maryland (1999)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Department of Disaster Information SpacesInstitute of Disaster Prevention Science and TechnologySanheChina
  2. 2.Science and Technology Information InstituteGeneral Research Institute for Nonferrous MetalsBeijingChina

Personalised recommendations