journal article·2020-12-24Open access

Dynamic Plan Control: An Effective Tool to Manage Demand Considering Mobile Internet Network Congestion

Xiaoyu Ma, Jihong Zhang, Yuan Cao, Zhou He, Jonas Nebel

Applied Sciences· Volume 11· Issue 1

Abstract

Rapidly increasing mobile data traffic have placed a significant burden on mobile Internet networks. Due to limited network capacity, a mobile network is congested when it handles too much data traffic simultaneously. In turn, some customers leave the network, which induces a revenue loss for the mobile service provider. To manage demand and maximize revenue, we propose a dynamic plan control method for the mobile service providers under connection-speed-restriction pricing. This method allows the mobile service provider to dynamically set the data plans’ availability for potential customers’ new subscriptions. With dynamic plan control, the service provider can adjust data network utilization and achieve high customer satisfaction and a low churn rate, which reflect high service supply chain performance. To find the optimal control policy, we transform the high-dimensional dynamic programming problem into an equivalent mixed integer linear programming problem. We find that dynamic plan control is an effective tool for managing demand and increasing revenue in the long term. Numerical evaluation with a large European mobile service provider further supports our conclusion. Furthermore, when network capacity or potential customers’ willingness to join the network changes, the dynamic plan control method generates robust revenue for the service provider.
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Research context

OpenAlex topics: Advanced Queuing Theory Analysis · Network Traffic and Congestion Control · Green IT and Sustainability

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

International Business School, Beijing Foreign Studies University, Beijing 100089, China

Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China

School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049, China

Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100049, China

Department of Industrial Engineering, Tsinghua University, Beijing 100084, China

Funding

National Natural Science Foundation of China

References (33)

Show source-provided references
1. Cisco (2019). Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017–2022 White Paper, Cisco. White Paper.
2. Hwang, R.H., Peng, M.C., and Cheng, K.C. (2019). QoS-guaranteed radio resource management in LTE-A co-channel networks with dual connectivity. Appl. Sci., 9.10.3390/app9153018 ↗
3. Optimal dynamic pricing of mobile data plans in wireless communications10.1016/j.omega.2016.02.001 ↗
4. Perez-Murueta, P., Gómez-Espinosa, A., Cardenas, C., and Gonzalez-Mendoza, M. (2019). Deep Learning System for Vehicular Re-Routing and Congestion Avoidance. Appl. Sci., 9.10.3390/app9132717 ↗
5. Statistical Learning of Service-Dependent Demand in a Multiperiod Newsvendor Setting10.1287/opre.2014.1303 ↗
6. Loiseau, P., Schwartz, G., Musacchio, J., and Amin, S. (2011, January 28–30). Incentive schemes for Internet congestion management: Raffles versus time-of-day pricing. Proceedings of the 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.10.1109/Allerton.2011.6120156 ↗
7. Lee, J., Yi, Y., Chong, S., and Jin, Y. (2013, January 14–19). Economics of WiFi offloading: Trading delay for cellular capacity. Proceedings of the 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Turin, Italy.10.1109/INFCOM.2013.6567156 ↗
8. Performance models of a proxy cache server: The impact of multimedia traffic10.1016/S0377-2217(02)00674-4 ↗
9. Cisco (2012). Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2011–2016, Cisco. White Paper.
10. Internet pricing and the history of communications10.1016/S1389-1286(01)00188-8 ↗
11. A Survey of Pricing Schemes in Wireless Networks10.1109/SURV.2011.060710.00028 ↗
12. How to Price Discriminate When Tariff Size Matters10.1287/mksc.1120.0720 ↗
13. Optimal Pricing of New Subscription Services: Analysis of a Market Experiment10.1287/mksc.21.2.119.147 ↗
14. Selling to Overconfident Consumers10.1257/aer.99.5.1770 ↗
15. Optimal Three-Part Tariff Plans10.1287/opre.2017.1609 ↗
16. On the Optimality of Three-Part Tariff Plans: When Does Free Allowance Matter?10.1287/opre.2018.1745 ↗
17. A survey of smart data pricing: Past proposals, current plans, and future trends10.1145/2543581.2543582 ↗
18. Smart data pricing: Using economics to manage network congestion10.1145/2756543 ↗
19. Jiang, L., Parekh, S., and Walrand, J. (2008, January 7–11). Time-Dependent Network Pricing and Bandwidth Trading. Proceedings of the NOMS Workshops 2008—IEEE Network Operations and Management Symposium Workshops, Salvador da Bahia, Brazil.10.1109/NOMSW.2007.33 ↗
20. Joe-Wong, C., Ha, S., and Chiang, M. (2011, January 20–24). Time-Dependent Broadband Pricing: Feasibility and Benefits. Proceedings of the 2011 31st International Conference on Distributed Computing Systems, Minneapolis, MN, USA.10.1109/ICDCS.2011.81 ↗
21. Batur, D., Ryan, J.K., Zhao, Z., and Vuran, M.C. (2019). Dynamic Pricing of Wireless Internet Based on Usage and Stochastically Changing Capacity. Manuf. Serv. Oper. Manag.10.1287/msom.2018.0727 ↗
22. TUBE: Time-dependent pricing for mobile data10.1145/2377677.2377723 ↗
23. Javaid, N., Ahmed, A., Iqbal, S., and Ashraf, M. (2018). Day ahead real time pricing and critical peak pricing based power scheduling for smart homes with different duty cycles. Energies, 11.10.3390/en11061464 ↗
24. Incentive Mechanisms for Internet Congestion Management: Fixed-Budget Rebate Versus Time-of-Day Pricing10.1109/TNET.2013.2270442 ↗
25. Demand estimation and assortment planning in wireless communications10.1007/s11518-015-5287-8 ↗
26. Forecasting day-ahead electricity load using a multiple equation time series approach10.1016/j.ejor.2015.12.030 ↗
27. Abbas, N., Yu, F., and Fan, Y. (2018). Intelligent video surveillance platform for wireless multimedia sensor networks. Appl. Sci., 8.10.3390/app8030348 ↗
28. Economics of WiFi Offloading: Trading Delay for Cellular Capacity10.1109/TWC.2014.010214.130949 ↗
29. Congestion-Optimal WiFi Offloading with User Mobility Management in Smart Communications10.1155/2018/9297536 ↗
30. Traffic offloading for online video service in vehicular networks: A cooperative approach10.1109/TVT.2018.2837024 ↗
31. When deep reinforcement learning meets 5G-enabled vehicular networks: A distributed offloading framework for traffic big data10.1109/TII.2019.2937079 ↗
32. Time-Dependent Pricing for Multimedia Data Traffic: Analysis, Systems, and Trials10.1109/JSAC.2019.2916490 ↗
33. Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans10.1016/j.ejor.2018.03.033 ↗