With recent advances on cellular technologies (such as 5G) that push the boundary of cellular performance, cellular reliability has become a key concern of cellular technology adoption and deployment. However, this fundamental concern has never been addressed due to the challenges of measuring cellular reliability on mobile devices and the cost of conducting large-scale measurements. This paper closes the knowledge gap by presenting the first large-scale, in-depth study on cellular reliability with more than 70 million Android phones across 34 different hardware models. Our study identifies the critical factors that affect cellular reliability and clears up misleading intuitions indicated by common wisdom. In particular, our study pinpoints that software reliability defects are among the main root causes of cellular data connection failures. Our work provides actionable insights for improving cellular reliability at scale. More importantly, we have built on our insights to develop enhancements that effectively address cellular reliability issues with remarkable real-world impact---our optimizations on Android's cellular implementations have effectively reduced 40% cellular connection failures for 5G phones and 36% failure duration across all phones.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of existing cellular infrastructures of base carriers, while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a two-year measurement study towards understanding various fundamental aspects of today's MVNO ecosystem, including its architecture, customers, performance, economics, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with one million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics, statistical modeling, and machine learning to address the MVNO's key concerns with regard to data usage prediction, data plan reselling, customer churn mitigation, and billing delay reduction. Our proposed techniques can help achieve higher revenues and improved services for commercial MVNOs.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of the existing cellular infrastructures of base carriers while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a nearly two-year measurement study towards understanding various key aspects of today's MVNO ecosystem, including its architecture, performance, economics, customers, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with \reviseabout 1 million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics and machine learning to optimize an MVNO's key businesses such as data plan reselling and customer churn mitigation. Our proposed techniques can help achieve %will lead to higher revenues and improved services for commercial MVNOs.