Mobile Network Failure Detection and Forecasting with Multiple User Bahavior

Abstract

Providing stable and high-quality service is a critical issue for mobile network service providers. However, due to an unexpectedly huge amount of data traffic exceeding network capacity of a provider, a mobile network service experiences severe failures such as network troubles, performance deterioration, and slow throughput. Then, the service users often detect service outages before the service provider detects them. They can immediately publish their impressions on the service through social media and search for failure information on the web. In this paper, we propose a machine learning approach that incorporates multiple user behavior data into detecting and forecasting failure events. The approach is based on novel feature extraction methods and a model ensemble method that combines outputs of supervised and unsupervised learning models from multiple user behavior datasets. We demonstrate the effectiveness of the approach by extensive experiments with real-world failure events.