Network management using database discovery tools

Mario Gerla, Ying-Dar Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

As the volume of network traffic increases due to the proliferation of distributed systems and the growth of real-time applications, a good understanding of traffic distribution and patterns becomes critical in network control and performance management. In this work, we upgrade the facilities of network management from traditional file systems to database and knowledge base systems and apply machine learning techniques to discover traffic patterns which are difficult to discern by human operators among a large volume of measurements. An experiment on intercdnnected LANs is conducted where some interesting patterns are found. The results show a strong traffic locality and some cyclic traffic patterns. The discovered rule base can describe the traffic distribution and patterns which need to be captured for any sophisticated performance management. The experiment has shown the high applicability of induction techniques to network management.

Original languageEnglish
Title of host publicationProceedings - 16th Conference on Local Computer Networks, LCN 1991
PublisherIEEE Computer Society
Pages378-385
Number of pages8
ISBN (Electronic)0818623705
DOIs
StatePublished - 1 Jan 1991
Event16th Conference on Local Computer Networks, LCN 1991 - Minneapolis, United States
Duration: 14 Oct 199117 Oct 1991

Publication series

NameProceedings - Conference on Local Computer Networks, LCN
Volume1991-October

Conference

Conference16th Conference on Local Computer Networks, LCN 1991
CountryUnited States
CityMinneapolis
Period14/10/9117/10/91

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