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Publikācija: Data Mining for Managing Intrinsic Quality of Service in MPLS

Publication Type Scientific article indexed in ERIH database, in INT1 or INT2 category journals
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Data Mining for Managing Intrinsic Quality of Service in MPLS
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Jans Jeļinskis
Gunārs Lauks
Keywords Data Mining, MPLS, LSP set up, QoS
Abstract LSP set up admission control policy is one of the notable problems that have to be solved to fulfill the requirements for effective resource allocation and network utilization for appropriate QoS level. In this paper, we verify a possibility of a new LSP setup admission algorithm, which uses optimization procedure based on multi-objective model with Pareto ranking and Genetic Algorithm. Decision rules are generated with Data Mining approach by performing classification operation to the selected data. This algorithm functions in two phases – classification and operating, which are accomplished consecutive. Algorithm is described and depicted. Experimental data are depicted and future research subjects are pointed. Ill. 4, bibl. 15
Reference Jeļinskis, J., Lauks, G. Data Mining for Managing Intrinsic Quality of Service in MPLS. Electronics and Electrical Engineering, 2008, No.5, pp.33-36. ISSN 1392-1215.
ID 3094