In an increasingly mobile connected world, our user experience of mobile applications more and more depends on the performance of cellular radio access networks (RANs). To achieve high quality of experience for the end user, it is imperative that operators effectively identify and diagnose performance problems quickly. In this paper, we describe our experience in understanding the challenges in automating the detection and diagnosis of performance problems in RANs. Working with a major cellular network operator on a part of their RAN that services more than 2 million users, we demonstrate that fine-grained modeling and analysis is the key towards this goal. We describe our methodology in analyzing RAN problems, and highlight a few of our findings, some previously unknown. We also discuss learnings from our attempt at building automated diagnosis solutions.
Published On: October 16, 2017
Presented At/In: 23rd ACM Annual International Conference on Mobile Computing and Networking (MobiCom 2017)