With outstanding improvement in telecommunication technologies, the number of possible offers and services has increased and due to competitive market in telecommunication, operators aimed to enhance their standards as much as possible. Considering millions line of logs every day it is very important to analyze log data. Vodafone Italia like other telecom companies is trying to keep the performance of its services and request processing in highest possible level. This project looks to improve the performance of the request processing in service provisioning and system operation team in Vodafone by developing a big data analytic solution on provisioning Business Process Management data using full text search engines like Elastic stack. This research aims to create a solution to collect, index, and visualize highly important requests and tasks data as soon as they are produced. This solution will help the provisioning team to have online insight to the performance indices of requests and their sub tasks. Also classifying requests and tasks to different categories of Mobile and Fix lines it will be easier to check the performance of each line separately. This project is developed using Elastic Stack, after performing the request and task KPI queries in Oracle, Logstash can index this data in Elasticsearch as a full-text search engine to analyze data. Kibana as visualization component is connected to Elasticsearch and visualize the indexed data and search results using different visualization types. The project is named Elastic KPI SASERR and is being used by the provisioning team to increase the performance of the service as much as possible. It has been developed in 4 month stage starting from March 2017 till end of June 2017, in first month the system is analyzed and backup information is gathered then in the second month the architecture is designed and the software engineering diagrams are drawn and planed. In month 3 and 4 the solution is tested and developed and, after successful result in virtual environment, the project is developed in real production environment in month 4 the developed project is also monitored for any anomalies and problems are solved.
Con il progredire delle tecnologie di telecomunicazione, il numero di servizi offerti è aumentato e, a causa di un mercato sempre più competitivo, gli operatori tendono il più possibile a migliorare i loro standard. Vodafone Italia è impegnata nel mantenere le prestazioni dei suoi servizi al più alto livello possibile e considerando la produzione di più di cento milioni di linee di log ogni giorno diviene molto importante analizzare e processare in maniera efficiente tali dati. Questo progetto si propone di migliorare le prestazioni nell'elaborazione delle richieste del team di fornitura dei servizi e di operazioni di sistema in Vodafone, sviluppando una soluzione Big Data analitica sui dati forniti dal Business Process Management. La ricerca mira a creare una soluzione per raccogliere, indicizzare e visualizzare i dati delle attività e le richieste di maggiore rilevanza non appena essi vengono prodotti e si propone di fornire una visione real time degli indici prestazionali delle richieste e delle sotto-attività. Il progetto, il cui nome è Elastic KPI SA SERR, è stato sviluppato utilizzando Elastic Stack che, grazie ai diversi componenti, dopo aver eseguito la richiesta e le attività di query KPI in Oracle Logstash, può indicizzare i dati in Elasticsearch come un motore di ricerca full-text. Kibana come componente di visualizzazione è collegato a Elasticsearch e viene utilizzato per visualizzare i dati indicizzati ed i risultati di ricerca utilizzando vari tipi di visualizzazione per eseguire le attività di monitoraggio e analisi necessarie al team di fornitura dei servizi. Esso è stato sviluppato durante uno stage di 4 mesi a partire da marzo 2017 e fino a fine giugno 2017. Nel primo mese viene analizzato il sistema e vengono raccolte le informazioni di backup, nel secondo mese l'architettura viene progettata e i diagrammi di ingegneria del software vengono disegnati e pianificati. Nel terzo e quarto mese la soluzione viene testata e sviluppata e, dopo l’esito positivo in ambiente virtuale, il progetto viene implementato nell’ambiente di produzione reale ed ivi monitorato e qualsiasi anomalia e possibile problema riscontrato viene risolto.
Big data analytics using elastic stack in provisioning system of sale and operation support in Vodafone Italia
BEHBOUDI VAIGHAN, FARDIN
2016/2017
Abstract
With outstanding improvement in telecommunication technologies, the number of possible offers and services has increased and due to competitive market in telecommunication, operators aimed to enhance their standards as much as possible. Considering millions line of logs every day it is very important to analyze log data. Vodafone Italia like other telecom companies is trying to keep the performance of its services and request processing in highest possible level. This project looks to improve the performance of the request processing in service provisioning and system operation team in Vodafone by developing a big data analytic solution on provisioning Business Process Management data using full text search engines like Elastic stack. This research aims to create a solution to collect, index, and visualize highly important requests and tasks data as soon as they are produced. This solution will help the provisioning team to have online insight to the performance indices of requests and their sub tasks. Also classifying requests and tasks to different categories of Mobile and Fix lines it will be easier to check the performance of each line separately. This project is developed using Elastic Stack, after performing the request and task KPI queries in Oracle, Logstash can index this data in Elasticsearch as a full-text search engine to analyze data. Kibana as visualization component is connected to Elasticsearch and visualize the indexed data and search results using different visualization types. The project is named Elastic KPI SASERR and is being used by the provisioning team to increase the performance of the service as much as possible. It has been developed in 4 month stage starting from March 2017 till end of June 2017, in first month the system is analyzed and backup information is gathered then in the second month the architecture is designed and the software engineering diagrams are drawn and planed. In month 3 and 4 the solution is tested and developed and, after successful result in virtual environment, the project is developed in real production environment in month 4 the developed project is also monitored for any anomalies and problems are solved.| File | Dimensione | Formato | |
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2017_10_Behboudi Vaighan.pdf
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Descrizione: thesis fardin behboudi vaighan 835787
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https://hdl.handle.net/10589/136446