Investigation of Mobile Network Traffic Using Hadoop and Mahout Machine Learning Methods

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.

Creator: 

Si, Man

Date: 

2015

Abstract: 

Since the emergence of mobile networks, the number of mobile subscriptions has continued to increase year after year. To efficiently assign wireless resources such as spectrum (which is rare and expensive), the network operator needs to process and analyze information and statistics about each base station and the traffic that passes through it. This thesis focuses on processing and analyzing two datasets provided by our industrial partner, Ericsson, Canada. A detailed approach that uses Apache Hadoop and the Mahout machine learning library to process and analyze the datasets is presented. The analysis provides insights to the network operator about the resource usage of network devices. This information is of great importance to network operators for efficient and effective management of resources and user experience. Furthermore, an investigation has been conducted that evaluates the impact of executing the Mahout clustering algorithms with various system and workload parameters on a Hadoop cluster.

Subject: 

Engineering - Electronics and Electrical

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).