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|Title:||Smart data pricing in wireless data networks : an economic solution to congestion||Authors:||Zhang, Liang||Advisors:||Wang, Dan (COMP)||Keywords:||Telecommunication -- Pricing.
|Issue Date:||2016||Publisher:||The Hong Kong Polytechnic University||Abstract:||As the popularity of smart mobile devices, together with bandwidth-intensive applications, the data traffic for wireless data networks has grown tremendously in the past few years. This poses challenges for the network operators to improve or even maintain network quality of the system. People are concerned whether such demand increase can be fulfilled by simple infrastructure expansion. On the other hand, it also brings huge financial burden to the Internet service providers (ISPs) since supporting such demand-supply gap requires large investments. The pricing of data traffic and other services is central to the core challenges of network management, growth sustainability and monetization supports. The smart data pricing has great potential to tackle the issue of surging demand for network operators and may also bring great benefit to consumers, Internet service providers, and content providers. In this thesis, we analyze the two main pricing proposals in smart data pricing, i.e., time dependent pricing (short for TDP) and sponsored data plan (short for SDP). We try to understand the rationale behind the two pricing models, as well as their impacts to the wireless data market, in particular, who will benefit and who will be hurt from these schemes. We also propose and analyze a new pricing proposal, time dependent sponsoring (short for TDS), that combines the advantages of TDP and SDP. First, we focus on the time dependent pricing, which is a promising pricing method to relieve the congestion caused by the surging traffic demand. TDP captures the time-variation characteristic of demand by charging users dynamically over time and has the potential to even out time-of-the-day fluctuations in bandwidth consumption. We explore the design space of TDP. In particular, we focus on a number of schemes, e.g., the usage-based scheme, the flat-rate scheme, and a mixture of them which called a cap scheme. Our main findings include: 1) the ISP obtains a higher profit with usage-based (or flat-rate) scheme if the capacity is insufficient (or sufficient); 2) the usage-based scheme usually achieves a higher consumer surplus and more efficient traffic utilization than the flat-rate scheme; and 3) the cap scheme is strongly preferred by the ISP to further increase its revenue.
Second, we analyze the sponsored data plan, a recent pricing proposal, i.e., when accessing contents from a particular content provider (short for CP), end users do not need to pay for that volume of traffic consumed, but the CP will sponsor for this data consumption. We build a two-class service model to analyze the consumers' traffic demand under the sponsored data plan with consideration of QoS. We use a two-stage Stackelberg game to characterize the interaction between CPs and the ISP and reveal a number of important findings. Our conclusions are: 1) When the ISP's capacity is sufficient, the sponsored data plan benefits consumers and CPs in the short run, but the ISP does not have incentives to further improve its service in the long run. 2) When ISP's capacity is insufficient,the ISP and end users may achieve a win-win trade, while the ISP and CPs always compete for the revenue. 3) The sponsored data plan may enlarge the unbalance in revenue distribution between different CPs; CPs with higher unit income and poorer technology support are more likely to prefer the sponsored data plan. Third, we propose and study one new smart data pricing scheme, time dependent sponsoring, i.e.,content providers can decide when and how much to sponsor their traffic.The main novelty of TDS is its potential to improve Internet resource utilization by migrating data traffic from peak times to valley times. We formulate a Stackelberg game model to study the interactions between the ISP, CPs and users, and derive the optimal sponsoring fractions over different times under TDS. In particular, we develop a dynamic programming algorithm to solve a non-convex optimization in sponsoring decisions. We find that: 1) TDS improves the CPs' bandwidth utilization,CPs' profit and consumers' welfare for slightly patient strategic users; but may result in controversial effects for highly patient strategic users, and 2) when CPs provide different subsidizations to different groups, the bandwidth usage can be improved significantly and so are CPs' profit and consumers' welfare, and 3) well designed TDS reduces the waste of capacity and thus improves social welfare and ISP's profit, compared with TDP.
|Description:||PolyU Library Call No.: [THS] LG51 .H577P COMP 2016 Zhang
xvi, 115 pages :color illustrations
|URI:||http://hdl.handle.net/10397/60070||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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