skip to main content
10.1145/3307334.3326070acmconferencesArticle/Chapter ViewAbstractPublication PagesmobisysConference Proceedingsconference-collections
research-article

An In-depth Study of Commercial MVNO: Measurement and Optimization

Published: 12 June 2019 Publication History

Abstract

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.

References

[1]
Ionut Andone, Konrad Blaszkiewicz, Mark Eibes, Boris Trendafilov, Christian Montag, and Alexander Markowetz. 2016. How Age and Gender Affect Smartphone Usage. In Proceedings of ACM UbiComp. Heidelberg, Germany.
[2]
Wenjie Bi, Meili Cai, Mengqi Liu, and Guo Li. 2016. A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn. IEEE Transactions on Industrial Informatics 12, 3 (June 2016), 1270--1281.
[3]
Hocine Bourennane, Dominique King, and Alain Couturier. 2000. Comparison of Kriging with External Drift and Simple Linear Regression for Predicting Soil Horizon Thickness with Different Sample Densities. Geoderma 97, 3--4 (September 2000), 255--271.
[4]
David S Broomhead and David Lowe. 1988. Radial Basis Functions, Multi-variable Functional Interpolation and Adaptive Networks. Technical Report. Royal Signals and Radar Establishment Malvern (United Kingdom). https://apps.dtic.mil/dtic/t r/fulltext/u2/a196234.pdf.
[5]
Kristof Coussement and Dirk Van den Poel. 2008. Churn Prediction in Subscription Services: An Application of Support Vector Machines while Comparing Two Parameter-selection Techniques. Expert Systems with Applications 34, 1 (January 2008), 313--327.
[6]
Philip J Davis and Philip Rabinowitz. 1984. Methods of Numerical Integration. Academic Press, 51--198.
[7]
Merouane Debbah, Loubna Echabbi, and Chahinez Hamlaoui. 2012. Market Share Analysis between MNO and MVNO under Brand Appeal Based Segmentation. In Proceedings of IEEE NetGCooP. Avignon, France.
[8]
Yadolah Dodge. 2008. Spearman Rank Correlation Coefficient. Springer New York, New York, NY, 502--505.
[9]
Huifang Feng and Yantai Shu. 2005. Study on Network Traffic Prediction Techniques. In Proceedings of IEEE WCNM. Wuhan, China.
[10]
Trevor Fiatal. 2012. Mobile Virtual Network Operator. US Patent 8,107,921.
[11]
A Gustavo González and M Ángeles Herrador. 2007. A Practical Guide to Analytical Method Validation, Including Measurement Uncertainty and Accuracy Profiles. Trends in Analytical Chemistry 26, 3 (January 2007), 227--238.
[12]
Henry Hsu and Peter A Lachenbruch. 2007. Paired T-test. Wiley Encyclopedia of Clinical Trials (September 2007), 1--3.
[13]
Junxian Huang, Feng Qian, Alexandre Gerber, Z Morley Mao, Subhabrata Sen, and Oliver Spatscheck. 2012. A Close Examination of Performance and Power Characteristics of 4G LTE Networks. In Proceedings of ACM MobiSys. Low Wood Bay, Lake District, UK.
[14]
Junxian Huang, Feng Qian, Yihua Guo, Yuanyuan Zhou, Qiang Xu, Z Morley Mao, Subhabrata Sen, and Oliver Spatscheck. 2013. An In-depth Study of LTE: Effect of Network Protocol and Application Behavior on Performance. ACM SIGCOMM Computer Communication Review 43, 4 (October 2013), 363--374.
[15]
Junxian Huang, Qiang Xu, Birjodh Tiwana, Z Morley Mao, Ming Zhang, and Paramvir Bahl. 2010. Anatomizing Application Performance Differences on Smartphones. In Proceedings of ACM MobiSys. San Francisco, CA, USA.
[16]
Shin-Yuan Hung, David C Yen, and Hsiu-Yu Wang. 2006. Applying Data Mining to Telecom Churn Management. Expert Systems with Applications 31, 3 (October 2006), 515--524.
[17]
Mathew Ingram. 2006. MVNOs: Phone Companies without the Equipment. https://www.theglobeandmail.com/technology/mvnos-phone-compa nies-without-the-equipment/article729367/.
[18]
Aditya Kapoor. 2017. Churn in the Telecom Industry - Identifying Customers Likely to Churn and How to Retain Them. https: //wp.nyu.edu/adityakapoor/2017/02/17/churn-in-the-telecom-industry-i dentifying-customers-likely-to-churn-and-how-to-retain-them/.
[19]
Pankaj Lanjudkar and Seapee Bajaj. 2017. Mobile Virtual Network Operator (MVNO) Market-Global Opportunity and Forecasts, 2017--2023. https://www.alli edmarketresearch.com/mobile-virtual-network-operator-market.
[20]
Zhenhua Li, Weiwei Wang, Tianyin Xu, Xin Zhong, Xiang-Yang Li, Yunhao Liu, Christo Wilson, and Ben Y Zhao. 2016. Exploring Cross-Application Cellular Traffic Optimization with Baidu TrafficGuard. In Proceedings of USENIX NSDI. Santa Clara, CA, USA.
[21]
Raul HC Lopes. 2011. Kolmogorov-Smirnov Test. Springer Berlin Heidelberg, Berlin, Heidelberg, 718--720.
[22]
Charalampos Meidanis, Ioannis Stiakogiannakis, and Maria Papadopouli. 2014. Pricing for Mobile Virtual Network Operators: The Contribution of U-map. In Proceedings of IEEE DySPAN. Mclean, VA, USA.
[23]
Anne Morris. 2015. Number of MVNOs Exceeds 1,000 Globally. https://www.fie rcewireless.com/europe/report-number-mvnos-exceeds-1-000-globally.
[24]
Akihiro Nakao, Ping Du, Yoshiaki Kiriha, Fabrizio Granelli, Anteneh Gebremariam, Tarik Taleb, and Miloud Bagaa. 2017. End-to-end Network Slicing for 5G Mobile Networks. Journal of Information Processing 25 (January 2017), 153--163.
[25]
Takashi Oshiba. 2018. Accurate Available Bandwidth Estimation Robust Against Traffic Differentiation in Operational MVNO Networks. In Proceedings of IEEE ISCC. Hague, Netherlands.
[26]
Marcin Owczarczuk. 2010. Churn Models for Prepaid Customers in the Cellular Telecommunication Industry Using Large Data Marts. Expert Systems with Applications 37, 6 (June 2010), 4710--4712.
[27]
Hassan Peyravi and Rahul Sehgal. 2017. Link modeling and delay analysis in networks with disruptive links. ACM Transactions on Sensor Networks 13, 4 (December 2017), 31.
[28]
Dulijana Popovic and Bojana Bacic. 2009. Churn Prediction Model in Retail Banking Using Fuzzy C-means Algorithm. Informatica 33, 2 (May 2009), 243--247.
[29]
Feng Qian, Alexandre Gerber, Zhuoqing Morley Mao, Subhabrata Sen, Oliver Spatscheck, and Walter Willinger. 2009. TCP Revisited: A Fresh Look at TCP in the Wild. In Proceedings of ACM IMC. Chicago, IL, USA.
[30]
Lenin Ravindranath, Sharad Agarwal, Jitendra Padhye, and Chris Riederer. 2014. Procrastinator: Pacing Mobile Apps' Usage of the Network. In Proceedings of ACM MobiSys. Bretton Woods, NH, USA.
[31]
David Rumelhart, Geoffrey Hinton, and Ronald Williams. 1986. Learning Representations by Back-propagating Errors. Nature 323, 6088 (October 1986), 533.
[32]
Paul Schmitt, Morgan Vigil, and Elizabeth Belding. 2016. A Study of MVNO Data Paths and Performance. In Proceedings of PAM. Heraklion, Crete, Greece.
[33]
Joel Sommers and Paul Barford. 2012. Cell vs. WiFi: On the Performance of Metro Area Mobile Connections. In Proceedings of ACM IMC. Boston, MA, USA.
[34]
Statista. 2018. Size of the Global MVNO Market from 2012 to 2022. https: //www.statista.com/statistics/671623/global-mvno-market-size/.
[35]
Narseo Vallina-Rodriguez, Srikanth Sundaresan, Christian Kreibich, Nicholas Weaver, and Vern Paxson. 2015. Beyond the Radio: Illuminating the Higher Layers of Mobile Networks. In Proceedings of ACM MobiSys. Florence, Italy.
[36]
Fatima Zarinni, Ayon Chakraborty, Vyas Sekar, Samir R Das, and Phillipa Gill. 2014. A First Look at Performance in Mobile Virtual Network Operators. In Proceedings of ACM IMC. Vancouver, BC, Canada.
[37]
Lizong Zhang, Nawaf R Alharbe, Guangchun Luo, Zhiyuan Yao, and Ying Li. 2018. A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction. Tsinghua Science and Technology 23, 4 (August 2018), 479--492.
[38]
Tianxiao Zhang, Huasen Wu, Xin Liu, and Longbo Huang. 2016. Learning-aided Scheduling for Mobile Virtual Network Operators with QoS Constraints. In Proceedings of IEEE WiOpt. Tempe, AZ, USA.

Cited By

View all
  • (2024)A Shortcut Through the IPX: Measuring Latencies in Global Mobile Roaming with Regional Breakouts2024 8th Network Traffic Measurement and Analysis Conference (TMA)10.23919/TMA62044.2024.10559001(1-10)Online publication date: 21-May-2024
  • (2024)Artificial Intelligence of Things: A SurveyACM Transactions on Sensor Networks10.1145/369063921:1(1-75)Online publication date: 30-Aug-2024
  • (2024)Untangling IoT Global Connectivity: The Importance of Mobile Signaling TrafficIEEE Transactions on Network and Service Management10.1109/TNSM.2024.341497521:4(4435-4449)Online publication date: Aug-2024
  • Show More Cited By

Index Terms

  1. An In-depth Study of Commercial MVNO: Measurement and Optimization

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        MobiSys '19: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
        June 2019
        736 pages
        ISBN:9781450366618
        DOI:10.1145/3307334
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Sponsors

        In-Cooperation

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 12 June 2019

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. churn mitigation
        2. data prediction
        3. machine learning
        4. mvno
        5. network performance

        Qualifiers

        • Research-article

        Funding Sources

        • the National Natural Science Foundation of China (NSFC) under grants
        • the National Key R&D Program of China under grant

        Conference

        MobiSys '19
        Sponsor:

        Acceptance Rates

        Overall Acceptance Rate 274 of 1,679 submissions, 16%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)38
        • Downloads (Last 6 weeks)7
        Reflects downloads up to 15 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)A Shortcut Through the IPX: Measuring Latencies in Global Mobile Roaming with Regional Breakouts2024 8th Network Traffic Measurement and Analysis Conference (TMA)10.23919/TMA62044.2024.10559001(1-10)Online publication date: 21-May-2024
        • (2024)Artificial Intelligence of Things: A SurveyACM Transactions on Sensor Networks10.1145/369063921:1(1-75)Online publication date: 30-Aug-2024
        • (2024)Untangling IoT Global Connectivity: The Importance of Mobile Signaling TrafficIEEE Transactions on Network and Service Management10.1109/TNSM.2024.341497521:4(4435-4449)Online publication date: Aug-2024
        • (2024)Enhancing Goal Achievement through Collaborative Goal Tracking: A Recommender System Approach2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT)10.1109/CSNT60213.2024.10545991(1118-1123)Online publication date: 6-Apr-2024
        • (2023)Fast Uplink Bandwidth Testing for Internet UsersIEEE/ACM Transactions on Networking10.1109/TNET.2023.323426531:4(1886-1901)Online publication date: Aug-2023
        • (2022)Recency effect and self-regulating design of mobile cellular systems in the context of interlaced generations: network bandwidth, power efficiency, and connection reliabilitySCIENTIA SINICA Informationis10.1360/SSI-2022-0062Online publication date: 7-Dec-2022
        • (2022)Mobile access bandwidth in practiceProceedings of the ACM SIGCOMM 2022 Conference10.1145/3544216.3544237(114-128)Online publication date: 22-Aug-2022
        • (2022)Global mobile network aggregatorsProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services10.1145/3498361.3538942(183-195)Online publication date: 27-Jun-2022
        • (2022)Performance Tradeoff of MVNOs in OFDMA-Based Virtualized Wireless NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2021.312431271:1(697-712)Online publication date: Jan-2022
        • (2022)A QoS Based Reliable Routing Mechanism for Service CustomizationJournal of Computer Science and Technology10.1007/s11390-021-0686-437:6(1492-1508)Online publication date: 30-Nov-2022
        • Show More Cited By

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media