Applying Fuzzy Computing Methods for On-line Monitoring of New Generation Network Elements
New generation networks belong to the class of big sophisticated heterogeneous hierarchical geographically distributed systems. Their functional characteristics, defining reliability, are the main characteristics which provide the application of these networks for their intended purpose. The paper offers the method of on-line functional monitoring of technical states of the new generation network elements based on application of a hierarchical fuzzy logical inference. The method and the generalized algorithm of on-line functional monitoring of technical states are developed. For realization of the offered method, the technology of intelligent agents is used. The functional structure of the intelligent agent is offered. The order of its interaction with a network element is considered. Results of modeling have shown a high efficiency of the offered approach. The possibility of the hardware-software realization of the offered method and algorithm a near real time mode is shown.
KeywordsNew generation network Situation network Monitoring Fuzzy logical inference
This work was partially supported by grants of RFBR (projects No. 16-29-09482, 18-07-01369, 18-07-01488), by the budget (the project No. AAAA-A16-116033110102-5), and by Government of Russian Federation (Grant 08-08).
- 1.ITU-T: General principles and general reference model for next generation networks. Recommendation Y.2011, Geneva (2004)Google Scholar
- 2.RFC 1450: Management information base for version 2 of the simple network management protocol (SNMP v2). IETF (1993)Google Scholar
- 3.Black, U.: Network Management Standards: SNMP, CMIP, TMN, MIBs and Objects Libraries. McGraw-Hill Inc., New York City (1995)Google Scholar
- 4.Saenko, I., Ageev, S., Kotenko, I.: Detection of traffic anomalies in multi-service networks based on a fuzzy logical inference. In: Intelligent Distributed Computing X. Studies in Computational Intelligence. Proceedings of 10th International Symposium on Intelligent Distributed Computing - IDC’2016, vol. 678, pp. 79–88. Springer International Publishing (2016)Google Scholar
- 5.Mamdani, E., Efstathion, H.: Higher-order logics for handling uncertainty in expert systems. Int. Man-Mach. Stud. 3, 243–259 (1985)Google Scholar
- 7.Stallings, W.: SNMP, SNMP v2, SNMP v3 and RMON 1 and 2, 3rd edn. Addison-Wesley, Reading (1998)Google Scholar
- 8.Harrington, D., Presuhn, R., Wijnen, B.: An architecture for describing SNMP management frameworks. IETF (1999)Google Scholar
- 10.Zhang-Shen, R., McKeown, N.: Guaranteeing quality of service to peering traffic. In: Proceedings of the Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2008), pp. 1472–1480 (2008)Google Scholar
- 12.Yager, R., Filev, D.: Essentials of Fuzzy Modeling and Control. Wiley, Hoboken (1984)Google Scholar
- 13.Azruddin, A., Gobithasan, R., Rahmat, B., Azman, S., Sureswaran, R.: A hybrid rule based fuzzy-neural expert system for passive network monitoring. In: Proceedings of the Arab Conference on Information Technology (ACIT), pp. 746–752 (2002)Google Scholar
- 16.Kotenko, I., Saenko, I., Ageev, S.: Countermeasure security risks management in the internet of things based on fuzzy logic inference. In: Proceedings of the 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2015), 20–22 August 2015, Helsinki, Finland, p. 655–659 (2015)Google Scholar
- 17.Nikolaev, A.B., Sapego, Yu.S, Jakubovich, A.N., Bernerb, L.I., Stroganovc, V.Yu.: Fuzzy algorithm for the detection of incidents in the transport system. Int. J. Environ. Sci. Educ. 11(16), 9039–9059 (2016)Google Scholar