TY - GEN
T1 - An In-depth study of commercial MVNO
T2 - 17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019
AU - Xiao, Ao
AU - Liu, Yunhao
AU - Li, Yang
AU - Qian, Feng
AU - Li, Zhenhua
AU - Bai, Sen
AU - Liu, Yao
AU - Xu, Tianyin
AU - Xin, Xianlong
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2019/6/12
Y1 - 2019/6/12
N2 - 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 about 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 higher revenues and improved services for commercial MVNOs.
AB - 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 about 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 higher revenues and improved services for commercial MVNOs.
UR - http://www.scopus.com/inward/record.url?scp=85069164426&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069164426&partnerID=8YFLogxK
U2 - 10.1145/3307334.3326070
DO - 10.1145/3307334.3326070
M3 - Conference contribution
AN - SCOPUS:85069164426
T3 - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
SP - 457
EP - 468
BT - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery, Inc
Y2 - 17 June 2019 through 21 June 2019
ER -