An Empirical Investigation of Mobile Network Traffic Data for Resource Management

Since the emergence of mobile networks, the number of mobile subscriptions has continued to increase year after year. To efficiently assign mobile network resources such as spectrum (which is expensive), the network operator needs to process and analyze information and statistics about each base station and the traffic that passes through it. This paper presents an application of data analytics by focusing on processing and analyzing two datasets from a commercial trial mobile network. A detailed description that uses Apache Hadoop and the Mahout machine learning library to process and analyze the datasets is presented. The analysis provides insights about the resource usage of network devices. This information is of great importance to network operators for efficient and effective management of resources and for supporting high-quality of 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. The results demonstrate the value of performance data analysis. Specifically, the execution time can be significantly reduced using data pre-processing and some machine learning techniques, and Hadoop. The investigation provides useful information for the network operators for future real-time data analytics.