Prototypical Design and Implementation of an Intelligent Network Data Analysis Tool Collaborating with Active Information Resource

  • Kazuto Sasai
  • Hideyuki Takahashi
  • Gen Kitagata
  • Tetsuo Kinoshita
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9799)

Abstract

The methodologies of data analysis such as data mining, statistical analysis and machine learning are important notion in the network management in order to deal with complex network structure and growing network threats. Although a number of analytics tools are available in present day, it is not easy to apply the tools for network management because the ordinal administrators do not have the professional knowledge about mathematics and statistics. To improve the knowledge problem of network management, in this paper, we propose an agent-based analysis support system for network data and its collaboration mechanism with autonomic network management system. The evaluation experiment using prototypical system shows that the system can reduce intellectual load of the analysis task of the users.

Keywords

AIR-NMS Data analysis Multiagent system Network data 
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References

  1. 1.
    Huang, Q., Shuang, K., Xu, P., Li, J., Liu, X., Su, S.: Prediction-based dynamic resource scheduling for virtualized cloud systems. J. Netw. 9(2), 375–383 (2014)Google Scholar
  2. 2.
    Scherrer, A., Larrieu, N., Owezarski, P., Borgnat, P., Abry, P.: Non-Gaussian and long memory statistical characterizations for internet traffic with anomalies. IEEE Trans. Dependable Secur. Comput. 4(1), 56–70 (2007)CrossRefGoogle Scholar
  3. 3.
    Ma, X., Hu, C., Chen, K.: Error tolerant address configuration for data center networks with malfunctioning devices. In: 32nd IEEE International Conference on Distributed Computing Systems, pp. 708–717 (2012)Google Scholar
  4. 4.
    El-Sayed, N., Stedanovici, I.A., Amvrosiadis, G., Hwang, A.A., Schroeder, B.: Temperature management in data centers: why some (might) like it hot. In: the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, pp. 163–174 (2012)Google Scholar
  5. 5.
    Demirkan, H., Delen, D.: Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decis. Support Syst. 55(1), 412–421 (2013)CrossRefGoogle Scholar
  6. 6.
    Chong, D., Shi, H.: Big data analytics: a literature review. J. Manag. Anal. 2(3), 175–201 (2015)Google Scholar
  7. 7.
  8. 8.
  9. 9.
  10. 10.
    Samaan, N., Karmouch, A.: Towards autonomic network management: an analysis of current and future research directions. IEEE Commun. Surv. Tutor. 11(3), 22–36 (2009)CrossRefGoogle Scholar
  11. 11.
    Flanders, M.: What is the biological basis of sensorimotor integration? Biol. Cybern. 104, 1–8 (2011)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Maggio, M., Hoffmann, H., Papadopulos, A.V., Panerati, J., Santambrogio, M.D., Agarwal, A., Leva, A.: Comparison of decision-making strategies for self-optimization in autonomic computing systems. ACM Trans. Auton. Adapt. Syst. 7(4), 36: 1–36: 32 (2012)CrossRefGoogle Scholar
  13. 13.
    Paton, N., de Aragão, M.A.T., Lee, K., Fernandes, A.A.A., Sakellariou, R.: Optimizing utility in cloud computing through autonomic workload execution. Bull. Techn. Committee Data Eng. 32(1), 51–58 (2009)Google Scholar
  14. 14.
    Kephart, J.O., Lechner, J.: A symbiotic cognitive computing perspective on autonomic computing. In: IEEE 12th International Conference on Autonomic Computing, pp. 109–114 (2015)Google Scholar
  15. 15.
    Sasai, K., Sveholm, J., Kitagata, G., Kinoshita, T.: A practical design and implementation of active information resource based network management system. Int. J. Energy Inf. Commun. 2(4), 67–86 (2011)Google Scholar
  16. 16.
    Kinoshita, T., Kitagata, G., Takahashi, H., Sasai, K., Kalegele, K.: An agent-based network management system using active information resources. Int. J. Adv. Smart Convergence 2(2), 10–15 (2013)CrossRefGoogle Scholar
  17. 17.
    Beszczad, A., Pagurek, B., White, T.: Mobile agents for network management. IEEE Commun. Surv. 1(1), 2–9 (1998)CrossRefGoogle Scholar
  18. 18.
    Terauchi, A., Akashi, O., Maruyama, M., Sugawara, T., Fukuda, K., Hirotsu, T., Kurihara, S., Koyanagi, K.: Agent organization system for multi-agent based network management. Trans. Jpn. Soc. Artif. Intell. 22(5), 482–492 (2007)CrossRefGoogle Scholar
  19. 19.
    Kalegele, K., Sasai, K., Takahashi, H., Kitagata, G., Kinoshita, T.: Four decades of data mining in network and systems management. IEEE Trans. Knowledge Data Eng. 27(10), 2700–2716 (2015)CrossRefGoogle Scholar
  20. 20.
    Kinoshita, T., Sugawara, K.: ADIPS framework for flexible distributed systems. In: Ishida, T. (ed.) PRIMA 1998. LNCS (LNAI), vol. 1599, p. 18. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  21. 21.
    Uchiya, T., Maemura, T., Li, X., Kinoshita, T.: Design and implementation of interactive design environment of agent system. In: Okuno, H.G., Ali, M. (eds.) IEA/AIE 2007. LNCS (LNAI), vol. 4570, pp. 1088–1097. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Shevtekar, A., Anantharam, K., Ansari, N.: Low rate TCP denial-of-service attack detection at edge routers. IEEE Commun. Lett. 9(4), 363–365 (2005)CrossRefGoogle Scholar
  23. 23.
    Csabai, I.: 1/f noise in computer network traffic. J. Phys. A Math. Gen. 27(12), L417 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kazuto Sasai
    • 1
  • Hideyuki Takahashi
    • 1
  • Gen Kitagata
    • 1
  • Tetsuo Kinoshita
    • 1
  1. 1.Research Institute of Electrical CommunicationTohoku UniversitySendaiJapan

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