Title :
Using a machine learning tool in diagnosis of network overload
Author :
Bisio, R. ; Gemello, R. ; Montariolo, E.
Author_Institution :
CSELT, Torino, Italy
Abstract :
Diagnosis of network anomalies is an important component of network management. A way to obtain a set of diagnostic rules by using a machine learning (ML) tool is described. In particular an overload situation is analyzed. Expert knowledge and a series of simulated network situations are employed together as inputs to a machine learning system. The ML tool helps the traffic engineer in proposing new rules and refining preexisting ones. The authors present a possible sequence of experiments that has led to a set of rules for recognizing a specific overload situation
Keywords :
diagnostic expert systems; learning (artificial intelligence); telecommunication network management; telecommunications computing; MERLINO; diagnosis; knowledge-based system; machine learning tool; network management; network overload; rules; telecommunication networks; Artificial intelligence; Automatic control; Communication system traffic control; Disaster management; Intelligent networks; Machine learning; Network synthesis; Telecommunication network management; Telecommunication traffic; Traffic control;
Conference_Titel :
Communications, 1992. ICC '92, Conference record, SUPERCOMM/ICC '92, Discovering a New World of Communications., IEEE International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-0599-X
DOI :
10.1109/ICC.1992.267979