Skip to main content
SpringerLink
Log in

Application of Bayesian Networks for Autonomic Network Management

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

Abstract

The ever evolving telecommunication networks in terms of their technology, infrastructure, and supported services have always posed challenges to the network managers to come up with an efficient Network Management System (NMS) for effective network management. The need for automated and efficient management of the current networks, more specifically the Next Generation Network (NGN), is the subject addressed in this research. A detailed description of the management challenges in the context of current networks is presented and then this work enlists the desired features and characteristics of an efficient NMS. It then proposes that there is a need to apply Artificial Intelligence (AI) and Machine Learning (ML) approaches for enhancing and automating the functions of NMS. The first contribution of this work is a comprehensive survey of the AI and ML approaches applied to the domain of NM. The second contribution of this work is that it presents the reasoning and evidence to support the choice of Bayesian Networks (BN) as a viable solution for ML-based NMS. The final contribution of this work is that it proposes and implements three novel NM solutions based on the BN approach, namely BN-based Admission Control (BNAC), BN-based Distributed Admission Control (BNDAC) and BN-based Intelligent Traffic Engineering (BNITE), along with the description of algorithms underpinning the proposed framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ali, E.S., Darwish, M.G.: Diagnosing network faults using bayesian and case-based reasoning techniques. In: International Conference on Computer Engineering & Systems, pp. 145–150. (2007)

  2. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)

    Google Scholar 

  3. Assaf, T., Dugan, J.B.: Decision automation for predictive analysis models. In: Symposium on Reliability and Maintainability, pp. 335–340. (2007)

  4. Auld, T., Moore, A.W., Gull, S.F.: Bayesian Neural Networks for Internet Traffic Classification. IEEE. Trans. Neural. Netw. 18(1), 223–239 (2007)

    Article  Google Scholar 

  5. Baldi, M., Baralis, E., Risso, F.: Data mining techniques for effective and scalable traffic analysis. In: IEEE Conference on Integrated Network Management, pp. 105–118. (2005)

  6. Bashar, A., Parr, G.P., McClean, S.I., Scotney, B.W., Subramanian, M., Chaudhari, S.K., Gonsalves, T.A.: Employing Bayesian belief networks for energy efficient network management. In: 16th IEEE National Conference on Communications (NCC 2010), pp. 1–5. (2010)

  7. Bashar, A., Parr, G.P., McClean, S.I., Scotney, B.W., Nauck, D.: Machine learning based call admission control approaches: A comparative study. In: 6th IEEE/IFIP International Conference on Network and Service Management (CNSM 2010), pp. 431–434. (2010)

  8. Bashar, A., Parr, G.P., McClean, S.I., Scotney, B.W., Nauck, D.: Performance analysis of bayesian networks-based distributed call admission control for NGN. In: 5th IEEE/IFIP Workshop on Distributed Autonomous Network Management System (DANMS 2012), pp. 1214–1220. (2012)

  9. Bisio, R., Gemello, R., Montariolo, E.: Using a machine learning tool in diagnosis of network overload. In: IEEE International Conference on Communications (ICC), pp. 1563–1567. (1992)

  10. Bouckaert, R. R. et al.: Weka manual (3.7.1). http://prdownloads.sourceforge.net/weka/WekaManual-3-7-1.pdf?download (2009). (Last accessed : Nov 2012)

  11. Burn-Thornton, K.E., Garibaldi, J., Mahdi, A.E.: Pro-active network management using data mining. In: IEEE Conference on Global Telecommunications (GLOBECOM 98), pp. 1208–1211. (1998)

  12. Calvert, K.L., Griffioen, J., Wen, S.: Scalable network management using lightweight programmable network services. J. Netw. Syst. Manage. 14(1), 15–47 (2006)

    Article  Google Scholar 

  13. Case, J., Fedor, M., Schoffstall, M., Davin, J.: Simple network management protocol. In: RFC 1157, IETF (1990)

  14. Cebulka, K.D., Muller, M.J., Riley, C.A.: Applications of AI for meeting network management challenges in the 1990s. In: IEEE GLOBECOM 1989, pp. 501–506. (1989)

  15. Chadha, R.: Applications of policy-based network management. In: Network Operations and Management Symposium (NOMS) 2004, pp. 907–908. (2004)

  16. Cheng, Y., Leon-Garcia, A., Foster, I.: Toward an Autonomic Service Management Framework: A Holistic Vision of SOA, AON, and Autonomic Computing. IEEE Commun. Mag. 46(5), 138–146 (2008)

    Article  Google Scholar 

  17. Choi, M., Hong, J.W.: Towards management of next generation networks. IEICE Transactions on Communications E90B, 3004–3014 (2007)

    Google Scholar 

  18. Claise, B.: Cisco Systems NetFlow Services Export Version 9. In: RFC 3954, IETF (2004)

  19. Claise, B.: IPFIX Protocol Specification. In: RFC 5101, IETF (2008)

  20. Clemm, A.: Network Management Fundamentals. 1st edn. Cisco Press, USA (2006)

    Google Scholar 

  21. Covo, A.A., Moruzzi, T.M., Peterson, E.D.: AI-assisted telecommunications network management. In: IEEE GLOBECOM 1989, pp. 487–491. (1989)

  22. De Paola A., et al.: Rule based reasoning for network management. In: International Workshop on Computer Architecture for Machine Perception, pp. 25–30. (2005)

  23. Deng, R.H., Lazar, A.A., Wang, W.: A probabilistic approach to fault diagnosis in linear lightwave networks. IEEE J. Select. Areas Commun. 11(9), 1438–1448 (1993)

    Article  Google Scholar 

  24. Ding, J.G., Kramer, B., Xu, S.H., Chen, H.S., Bai, Y.C.: Predictive fault management in the dynamic environment of IP networks. In: IEEE Workshop on IP Operations and Management Proceedings (IPOM 2004), pp. 233–239. (2004)

  25. Dini, P., Hasan, M.Z., Morrow, M., Parr, G., Rolin, P.: IP/MPLS OAM: challenges and directions. In: IEEE Workshop on IP Operations and Management, pp. 1–8. (2004)

  26. Ekaette, E.U., Far, B.H.: A framework for distributed fault management using intelligent software agents. In: IEEE Canadian Conference on Electrical and Computer Engineering 2003, vol. 2, pp. 797–800. (2003)

  27. Enns, R.: Netconf Configuration Protocol. In: RFC 4741, IETF (2006)

  28. Ezawa, K.J., Norton, S.W.: Constructing Bayesian networks to predict uncollectible telecommunications accounts. IEEE Exp. 11(5), 45–51 (1996)

    Article  Google Scholar 

  29. Fan, C.F., Yu, Y.C.: BBN-based software project risk management. J. Syst. Softw. 73(2), 193–203 (2004)

    Article  Google Scholar 

  30. Guo, D., Wang, X.: Bayesian network loss inference. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 33–36. (2003)

  31. Harrington, D., Presuhn, R., Wijnen, B.: An architecture for describing SNMP management frameworks. In: RFC 3411, IETF (2002)

  32. Heckerman, D.: A tutorial on learning with Bayesian networks. 1st edn. MIT Press, Cambridge (1999)

    Google Scholar 

  33. Hood, C.S., Ji, C.: Proactive network fault detection. In: IEEE INFOCOM 1997, pp. 1147–1155. (1997)

  34. Hugin Expert A/S: Hugin Researcher 7.3. http://www.hugin.com. (Accessed : Nov 2012)

  35. IBM: SPSS Modeler 13.0. http://www.spss.com. (Last accessed : Nov 2012)

  36. Jensen, F.: Bayesian Networks and Decision Graphs. 2nd edn. Springer Co., USA (2007)

    Book  MATH  Google Scholar 

  37. Jensen, F.V., Lauritzen, S.L., Olesen, K.G.: Bayesian updating in causal probabilistic networks by local computations. Comput. Stat. Quat. 4(1), 269–282 (1990)

    MathSciNet  Google Scholar 

  38. Jeon, G., Falcon, R., Kim, D., Lee, R., Jeong, J.: Application of Bayesian Belief Network in Reliable Analysis for Video Deinterlacing. IEEE Trans. Consum. Electron. 54(1), 123–130 (2008)

    Article  Google Scholar 

  39. Jia, L., Zhu, W., Zhai, C., Du, Y.: Research on an Integrated Network Management System. In: International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), pp. 311–316. (2007)

  40. Jun, L., Shunyi, Z., Yanqing, L., Zailong, Z.: Internet traffic classification using machine learning. In: International Conference on Communications and Networking, pp. 239–243. (2007)

  41. Kataria, D., Logothetis, D.: Fixed mobile convergence: network architecture, services, terminals, and traffic management. In: 16th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2005), pp. 2289–2300. (2005)

  42. Khan, M.J., Shamail, S., Awais, M.M., Hussain, T.: Comparative study of various artificial intelligence techniques to predict software quality. In: IEEE Multitopic Conference, INMIC 2006, pp. 173–177. (2006)

  43. Khanafer, R.M., Solana, B., Triola, J., Barco, R., Moltsen, L., Altman, Z., Lazaro, P.: Automated Diagnosis for UMTS Networks Using Bayesian Network Approach. IEEE Trans. Veh. Technol. 57(4), 2451–2461 (2008)

    Article  Google Scholar 

  44. Lauritzen, S.L.: The EM algorithm for graphical association models with missing data. Comput. Stat. Data Anal. 19(2), 191–201 (1995)

    Article  MATH  Google Scholar 

  45. Lee, C., Knight, D.: Realization of the next-generation network. IEEE Commun. Mag. 43(10), 34–41 (2005)

    Article  Google Scholar 

  46. Li, M., Sandrasegaran, K.: Network management challenges for next generation networks. In: 30th IEEE Conference on Local Computer Networks, pp. 1–6 (2005)

  47. Liu, G., Ji, C.: Resilience of all-optical network architectures under in-band crosstalk attacks: a probabilistic graphical model approach. IEEE J. Select. Areas Commun. 25(3), 2–17 (2007)

    Article  Google Scholar 

  48. Livadas, C., Walsh, R., Lapsley, D., Strayer, W.T.: Using ML techniques to identify botnet traffic. In: IEEE Conference on Local Computer Networks, pp. 967–974. (2006)

  49. Luo, J.Z., Gu, G.Q., Fei, X.: An architectural model for intelligent network management. J. Comput. Sci. Technol. 15(2), 136–143 (2000)

    Article  Google Scholar 

  50. McClean, S.I.: Data mining and knowledge discovery. Encyclopaedia of Physical Science and Technology 4(1), 229–246 (2002)

    Google Scholar 

  51. Modarressi, A.R., Mohan, S.: Control and management in next-generation networks: challenges and opportunities. IEEE Commun. Mag. 38(10), 94–102 (2000)

    Article  Google Scholar 

  52. Muller, C., Magill, E.H., Prosser, P., Smith, D.G.: Artificial intelligence in telecommunications. In: IEEE GLOBECOM 1993, pp. 883–887. (1993)

  53. Nguyen, T.T.T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surveys Tutorials 10(4), 56–76 (2008)

    Article  Google Scholar 

  54. Open systems interconnection (OSI) Common Management Information Protocol: specification. ITU-T Recommendation X.711 (1997)

  55. Parakhine, A., O’Neill, T., Leaney, J.: Application of Bayesian Networks to Architectural Optimisation. In: IEEE International Conference on Engineering of Computer-Based Systems, pp. 37–44 (2007)

  56. Phillips, I., Parish, D., Sandford, M., Bashir, O., Pagonis, A.: Architecture for the management and presentation of communication network performance data. IEEE Trans. Instrum. Meas. 55(3), 931–938 (2006)

    Article  Google Scholar 

  57. Pras, A., Schoenwaelder, J., Burgess, M., Festor, O., Perez, G.M., Stadler, R., Stiller, B.: Key research challenges in network management. IEEE Commun. Mag. 45(10), 104–110 (2007)

    Article  Google Scholar 

  58. Prieto, A.G., Stadler, R.: Adaptive Distributed Monitoring with Accuracy Objectives. In: ACM SIGCOMM Workshop on Internet Network Management (INM 06), pp. 65–70. (2006)

  59. Qi, J., Wu, F., Li, L., Shu, H.: Artificial intelligence applications in the telecommunications industry. Expert Syst. 24(4), 271–291 (2007)

    Article  Google Scholar 

  60. Samaan, N., Karmouch, A.: Towards Autonomic Network Management: an Analysis of Current and Future Research Directions. IEEE Commun. Surveys Tutorials 11(3), 22–36 (2009)

    Article  Google Scholar 

  61. Sasisekharan, R., Seshadri, V., Weiss, S.M.: Data mining and forecasting in large-scale telecommunication networks. IEEE Expert 11(1), 37–43 (1996)

    Article  Google Scholar 

  62. Sohail, S., Khanum, A.: Simplifying network management with fuzzy logic. In: IEEE International Conference on Communications (ICC 2008), pp. 195–201 (2008)

  63. Spiegelhalter, D.J., Lauritzen, S.L.: Sequential updating of conditional probabilities on directed graphical structures. Networks, Special Issue on Influence Diagrams 20(5), 579–605 (1990)

    MATH  MathSciNet  Google Scholar 

  64. Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. 2nd edn. MIT Press, (2001)

    MATH  Google Scholar 

  65. Steck, H.: Constrained-based structural learning in Bayesian networks using finite data sets. Ph.D. thesis, Department of Informatics, Technical University Munich (2001)

  66. Strassner, J., Menich, B.J.: Philosophy and methodology for knowledge discovery in autonomic computing systems. In: International Workshop Database and Expert Systems Applications, pp. 738–743 (2005)

  67. Sun, S., Zhang, C., Yu, G.: A bayesian network approach to traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems 7(1), 124–132 (2007)

    Google Scholar 

  68. Sun, Y.T., Tsang, K.F., Tung, H.Y., Lam, K.L., Ko, K.T.: QoS prediction for High Speed Packet Access Networks. In: IEEE Consumer Communications and Networking Conference, pp. 788–789 (2008)

  69. Tele Management Forum : NGOSS and eTOM overview. http://www.itarchitects.ca/whitepaper/TeleManagementForum/NGOSSandeTOM.pdf (2008). (Last accessed : May 2012)

  70. TMN Management Functions. ITU-T Recommendation M.3400 (2000)

  71. Tong, H., Brown, T.X.: Reinforcement Learning for Call Admission Control and Routing under Quality of Service. Mach. Learn. 49(2-3), 111–139 (2002)

    Article  MATH  Google Scholar 

  72. Waldbusser, S., Cole, R., Kalbfleisch, C., Romascanu, D.: Introduction to the Remote Monitoring (RMON) Family of MIB Modules. In: RFC 3577, IETF (2003)

  73. Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. SIGCOMM Comput. Commun. Rev. 36(5), 5–16 (2006)

    Article  Google Scholar 

  74. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. 2nd edn. Morgan Kaufmann Series, CA, USA (2005)

    Google Scholar 

  75. Wu, J., Zhou, J., Yan, P., Wu, M.: A study on network fault knowledge acquisition based on support vector machine. In: International Conference on Machine Learning and Cybernetics, pp. 3893–3898 (2005)

  76. Yang, Y., Webb, G., Wu, X.: Discretization methods. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook., pp. 113–130. Springer, Berlin (2005)

    Chapter  Google Scholar 

  77. Zander, S., Nguyen, T., Armitage, G.: Automated traffic classification and application identification using machine learning. In: IEEE Conference on Local Computer Networks, pp. 250–257 (2005)

  78. Zhang, R., Bivens, A.: Comparing the use of Bayesian networks and neural networks in response time modeling for service-oriented systems. In: ACM Workshop on Service-Oriented Computing Performance, SOCP 2007, pp. 67–74 (2007)

Download references

Acknowledgements

The authors would like to acknowledge the support of Prince Mohammad Bin Fahd University, Saudi Arabia, for providing the facilities to perform this research work. The Vice Chancellors Research Scholarship support provided by the University of Ulster, UK during the first author's PhD research is highly appreciated. A special thanks to British Telecom, British Council UKIERI and IU-ATC (http://www.iu-atc.com) for funding the research internships at IIT Madras, India and BT Adastral Park, UK during this joint research work.

Author information

Authors and Affiliations

  1. College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al-Khobar, 31952, Saudi Arabia

    Abul Bashar

  2. School of Computing and Information Engineering, University of Ulster, Coleraine, BT52 1SA, UK

    Gerard Parr, Sally McClean & Bryan Scotney

  3. Service and Operations, BT Technology, Adastral Park, Ipswich, IP5 3RE, UK

    Detlef Nauck

Authors
  1. Abul Bashar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerard Parr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sally McClean
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bryan Scotney
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Detlef Nauck
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abul Bashar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bashar, A., Parr, G., McClean, S. et al. Application of Bayesian Networks for Autonomic Network Management. J Netw Syst Manage 22, 174–207 (2014). https://doi.org/10.1007/s10922-013-9289-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10922-013-9289-x

Keywords

Search

Navigation