Postupci automatizirane analize velikih količina mrežnih podataka

master's thesis

• Title (english): Procedures for automated analysis of large volumes of network data
• Author: Petar Marić
• Mentor: Mirko Randić (mentor)
• Committee member: Mirko Randić (predsjednik povjerenstva)
• Committee member: Boris Vrdoljak (član povjerenstva)
• Committee member: Damir Pintar (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2016-07-13, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Rast količine podataka koji opisuju rad komunikacijskih mreža, dovodi do problema efikasnosti i vremenske isplativosti pri sekvencijalnoj obradi podataka. Radi toga budućnost, ali i sadašnjost, analize mrežnih podataka je u raspodijeljenoj obradi podataka. Jedna od platformi za raspodijeljenu obradu podataka je Apache Hadoop platforma.
U ovom radu, analizirani podaci nisu iz stvarne mreže, nego je korišten generator mrežnih podataka OpenMSC. OpenMSC generator generira kontrolne mrežne pakete simbolički, ali za svrhu istraživanja mogućnosti izgradnje sustava za analizu rada mreže, takav izvor podataka je zadovoljavajući.
Program za analizu podataka koji se izvodi na Hadoop platformi napisan je u programskom jeziku Java na principu programskog modela MapReduce. Izlazni podaci dobiveni iz programa su statistički podaci koji opisuju pojedine karakteristike rada emulirane mreže koji dijelom ukazuju i na probleme i nepravilnosti u radu.

Abstract (english)

The growth in the amount of data that describe the operation of communication networks, leads to problems of efficiency and time cost effectiveness of sequential processing. Therefore, the future, or the present, of network data analysis lies in distributed data processing. One of the platform for distributed data processing is Apache Hadoop platform.
In this thesis, analyzed data are not from the real network, but it is used data network generator OpenMSC. OpenMSC generator generates a symbolic control network packets, but for exploring the possibility of building system for the network analysis, such source of information is satisfactory.
Data analysis program that runs on Hadoop platform was written in Java in MapReduce programming model. Output data obtained from the program are statistical data that describes the specific characteristics of emulated network that partly indicate of the problems and irregularities.

• Keywords: Hadoop; OpenMSC; MapReduce; YARN; Komunikacijska mreža; Upravljanje mrežom; Anomalije
• Keywords (english): Hadoop; OpenMSC; MapReduce; YARN; Communication network; Network management; Anomalies
• Language: croatian
• URN:NBN: urn:nbn:hr:168:254147
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2018-02-06 10:25:44