Alarm logs are a valuable source of information and play a crucial role in network management. Network devices such as backbone routers or 3G/4G base stations generate verbose and detailed logs that network managers process to detect problems and identify their root causes. Manual analysis of such logs is extremely time-consuming because of the extensive amount of data. Therefore, finding suitable automatic methods to process logs is an important problem in the network analysis area.In this paper, we target the automatic extraction of situations, i.e., sequences of events occurring close in time and space which identify common and recurring patterns. We adopt an unsupervised machine learning approach to automatically mine logs and provide information and correlations in network failures. We face a real use case processing more than 2 million alarms generated by 2 months of TIM Network Operations Center in Northern Italy. Most of the features are categorical and call for specific methodologies to process them. We choose rule mining of frequent items. We focus on event logs and apply rule mining methods to extract temporal-spatial correlations and co-occurrences, i.e., situations. To ease the analyst work, we highlight the most important rules and offer visualization techniques in both spatial and temporal dimensions. Results have been verified to be helpful to recognize common situations and identify possible future anomalies.

Mining Patterns in Mobile Network Logs / Zargarian, Golnaz; Vassio, L; Munafo, Maurizio; Mellia, M. - STAMPA. - (2019), pp. 1-6. (Intervento presentato al convegno 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) tenutosi a Arlington, VA (USA) nel Aprile 2019).

Mining Patterns in Mobile Network Logs

Vassio, L;Munafo, Maurizio;Mellia, M
2019

Abstract

Alarm logs are a valuable source of information and play a crucial role in network management. Network devices such as backbone routers or 3G/4G base stations generate verbose and detailed logs that network managers process to detect problems and identify their root causes. Manual analysis of such logs is extremely time-consuming because of the extensive amount of data. Therefore, finding suitable automatic methods to process logs is an important problem in the network analysis area.In this paper, we target the automatic extraction of situations, i.e., sequences of events occurring close in time and space which identify common and recurring patterns. We adopt an unsupervised machine learning approach to automatically mine logs and provide information and correlations in network failures. We face a real use case processing more than 2 million alarms generated by 2 months of TIM Network Operations Center in Northern Italy. Most of the features are categorical and call for specific methodologies to process them. We choose rule mining of frequent items. We focus on event logs and apply rule mining methods to extract temporal-spatial correlations and co-occurrences, i.e., situations. To ease the analyst work, we highlight the most important rules and offer visualization techniques in both spatial and temporal dimensions. Results have been verified to be helpful to recognize common situations and identify possible future anomalies.
2019
978-1-7281-0618-2
File in questo prodotto:
File Dimensione Formato  
190304.pdf

non disponibili

Descrizione: Versione pubblicata
Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 464.69 kB
Formato Adobe PDF
464.69 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Vassio_AnNet2019_CameraReady.pdf

accesso aperto

Descrizione: Versione camera-ready
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 537.44 kB
Formato Adobe PDF
537.44 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2800192