Treffer: MACHINE LEARNING TECHNIQUES FOR DATA CENTER ANOMALIES IDENTIFICATION.

Title:
MACHINE LEARNING TECHNIQUES FOR DATA CENTER ANOMALIES IDENTIFICATION.
Authors:
DINU, Bogdan1, ARSENE, Octavian1
Source:
Annals of 'Constantin Brancusi' University of Targu-Jiu. Engineering Series / Analele Universităţii Constantin Brâncuşi din Târgu-Jiu. Seria Inginerie. 2014, Issue 3, p35-39. 5p.
Subject Terms:
Machine learning, Server farms (Computer network management), Anomaly detection (Computer security), Support vector machines, Data mining
Database:
Supplemental Index

Weitere Informationen

One of the most important tasks within a Data Center is to monitoring computers. The present paper introduces an improved data mining process flow in order to speed it up and to avoid anomalous data to be part of the presentation layer. The collection process is using a parallel approach implemented in a PL/SQL package. The anomaly detection phase is using two different mechanisms, a probabilistic model in Octave implementation and a Support Vector Machine algorithm using Oracle Data Mining product. [ABSTRACT FROM AUTHOR]