SDP research papers can be roughly categorized by the different types of pricing that they investigate. We consider 15 such topics here. If you know of a paper we missed, let us know.
Time-dependent pricing (11)
In time-dependent pricing, users are charged according to the time of the day at which data is consumed, which serves as a rough proxy for network congestion. Users then have an incentive to shift their usage to cheaper times of the day when there is less traffic on the network, even-ing out the distribution of network demand over the day. Time-dependent pricing, however, does not explicitly make prices dependent on congestion in the network, instead estimating network congestion based on the time of the day and historical usage patterns. The simplest such pricing in practice is a two-period plan that charges different rates during the daytime and nighttime, as was implemented for voice calls in the U.S. before 2010. A more sophisticated variant, day-ahead pricing, computes the prices one day in advance based on the network congestion observed at different times over the previous day.
John Tadrous, Atilla Eryilmaz, and Hesham El Gamal IEEE/ACM Transactions on Networking, 2015
In this work, we formulate and study the profit maximization problem for a wireless service provider (SP) that encounters time-varying, yet partially predictable, demand characteristics. The disparate demand levels throughout the course of the day yield excessive service cost in the peak hour that substantially hurts the reaped profit. With the SP's ability to track and statistically predict future requests of its users, we propose to enable proactive caching of the peak hour demand ahead during off-peak times. Thus, network traffic will be smoothed out, while end-users' activity patterns are undisturbed. In addition, the SP is able to assign personalized pricing policies that strike the best balance between enhancing the certainty about the future demand for optimal proactive caching and maximizing the revenue collected from end-users. Comparing the proposed system's performance to the baseline scenario of the existing practice of no-proactive service, we show that the SP attains profit gain that grows with number of users, at least, as the first derivative of the cost function. Moreover, end-users that receive proactive caching services make strictly positive savings. Thus, we essentially demonstrate the win-win situation to be reaped through the exploitation of the consistent users' activity.
C. Joe-Wong, S. Sen, S. Ha and M. Chiang Passive and Active Measurement Conference 2015
The growing amount of traffic in mobile data networks is causing concern for Internet service providers (ISPs), especially smaller ISPs that need to lease expensive links to Tier 1 networks. Large amounts of traffic in “peak” hours are of especial concern, since network capacity must be provisioned to accommodate these peaks. In response, many ISPs have begun trying to influence user behavior with pricing. Time-dependent pricing (TDP) can help reduce peaks, since it allows ISPs to charge higher prices during peak periods. We present results from the first TDP trial with a commercial ISP. In addition to analyzing application-specific mobile and WiFi traffic, we compare changes in user behavior due to monthly data caps and time-dependent prices. We find that monthly data caps tend to reduce usage, while TDP can increase usage as users consume more data during discounted times. Moreover, unlike data caps, TDP reduces the network’s peak-to-average usage ratio, lessening the need for network over-provisioning and increasing ISP profit.
C. Parris, S. Keshav and D. Ferrari Tech. rep. TR-92-016. Tenet Group, ICSI, University of California, Berkeley, CA, 1992
Integrated networks of the near future are expected to provide a wide variety of services, which could consume widely differing amounts of resources. We present a framework for pricing services in integrated networks, and study the effect of pricing on user behavior and network performance. We first describe a network model that is simple, yet models details such as the wealth distribution in society, different classes of service, peak and off-peak traffic, elasticity of user’s demand, and call blocking due to budgetary constraints. We then perform experiments to study the effect of setup, per packet and peak load prices on the blocking probability of two classes of calls passing through a single node enforcing admission control. Some selected results are that a) increasing prices first increases the net revenue to a provider, then causes a decrease b) peak-load pricing spreads network utilization more evenly, raising revenue while simultaneously reducing call blocking probability. Finally, we introduce a novel metric for comparing pricing schemes, and prove that for the most part, a pricing scheme involving setup prices is better than a pricing scheme without such a component.
C. Parris and D. Ferrari Tech. rep. TR-92-018. Tenet Group, ICSI, University of California, Berkeley, CA, 1992
In the packet switching networks of the future the need for guaranteed performance on a wide variety of traffic characteristics will be of paramount importance. The generation of revenue, to recover costs and provide profit, and the multiple type of services offered will require that new pricing policies be implemented. This paper presents a resource based pricing policy for real-time channels ( ie., channels with guaranteed performance ) in a packet switching network. The policy is based on a set of specific criteria, and the charges for any channel are based on the resources reserved for use by the channel. This reservation charge is based on the type of service requested, the time of day during which the channel exists, and the lifetime of the channel. We argue that the traditional resources are not sufficient to determine a fair reservation charge for a channel offering guaranteed delay bounds, and we introduce the notion of a delay resource in our charging formula. The type of service requested is thus characterized by the amount of the bandwidth, buffer space, CPU, and delay resources reserved. The analysis of this pricing policy is reduced to the analysis of a single node of the network, assuming a homogeneous network. This single-node characteristic increases the scalability and flexibility of the policy. An example of an implementation of this policy is provided.
P. Loiseau, G. Schwartz, J. Musacchio, S. Amin and S. S. Sastry IEEE/ACM Transactions on Networking 22(2): 647—661, 2014
Mobile data traffic has been steadily rising in the past years. This has generated a significant interest in the deployment of incentive mechanisms to reduce peak-time congestion. Typically, the design of these mechanisms requires information about user demand and sensitivity to prices. Such information is naturally imperfect. In this paper, we propose a fixed-budget rebate mechanism that gives each user a reward proportional to his percentage contribution to the aggregate reduction in peak-time demand. For comparison, we also study a time-of-day pricing mechanism that gives each user a fixed reward per unit reduction of his peak-time demand. To evaluate the two mechanisms, we introduce a game-theoretic model that captures the public good nature of decongestion. For each mechanism, we demonstrate that the socially optimal level of decongestion is achievable for a specific choice of the mechanism's parameter. We then investigate how imperfect information about user demand affects the mechanisms' effectiveness. From our results, the fixed-budget rebate pricing is more robust when the users' sensitivity to congestion is “sufficiently” convex. This feature of the fixed-budget rebate mechanism is attractive for many situations of interest and is driven by its closed-loop property, i.e., the unit reward decreases as the peak-time demand decreases.
J. M. Dyaberi, B. Parsons, V. S. Pai, K. Kannan, Y. R. Chen, R. Jana, D. Stern and A. Varshavsky IEEE Communications Magazine, 50(11): 100-107, 2012
Mobile data traffic is expected to grow exponentially in the next few years due to the explosive growth of mobile web and video traffic on smartphones. Wireless operators have invested heavily to make infrastructural improvements by installing new cell towers and offloading cellular data traffic to Wi-Fi to resolve congestion. They are also exploring the use of behavioral and economic interventions to manage congestion. To understand the role of interventions, we distributed smartphones to students at Purdue University, loaded with applications to perform monitoring and location tracking with user consent. We conducted two experiments: first with 14 phones of one type, then with 30 phones of two types. Wi-Fi traffic and cellular network data usage were collected and analyzed to characterize and quantify the changes in usage behaviors; the second experiment also captured location data during compliance/non-compliance to incentive messages. The trial seeks not only to experiment with incentives and disincentives to observe their effectiveness, but also to understand current mobile broadband and Wi-Fi usage behaviors in a campus environment. Our results indicate a high level of compliance with economic incentives and disincentives. Detailed analysis further showed correlation with two psychological measures of each user (agreeableness and neuroticism). In addition, we found schemes with probabilistic payments of higher incentive amounts getting more positive results compared to schemes with definite payments with lower incentive amounts, despite similar total payout.
L. Zhang, W. Wu and D. Wang Proceedings of IEEE INFOCOM, 2014
With the advances of bandwidth-intensive mobile devices, we see severe congestion problems in wireless data networks. Recently, research emerges to solve this problem from a pricing point of view. Time dependent pricing has been introduced, and initial investigations have shown its advantages over the conventional time independent pricing. Nevertheless, much is unknown in how a practical and effective time dependent pricing scheme can be designed. In this paper, we explore the design space of time dependent pricing. 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 we called a cap scheme. Our 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. We believe our findings provide important insights for ISPs to design effective pricing schemes.
C. Joe-Wong, S. Ha and M. Chiang Proceedings of IEEE ICDCS, 2011
Charging different prices for Internet access at different times induces users to spread out their bandwidth consumption across times of the day. Potential impact on ISP revenue, congestion management, and consumer behavior can be significant, yet some fundamental questions remain: is it feasible to operate time dependent pricing and how much benefit can it bring? We develop an efficient way to compute the cost-minimizing time-dependent prices for an Internet service provider (ISP), using both a static session-level model and a dynamic session model with stochastic arrivals. A key step is choosing the representation of the optimization problem so that the resulting formulations remain computationally tractable for large-scale problems. We next show simulations illustrating the use and limitation of time-dependent pricing. These results demonstrate that optimal prices, which "reward'' users for deferring their sessions, roughly correlate with demand in each period, and that changing prices based on real-time traffic estimates may significantly reduce ISP cost. The degree to which traffic is evened out over times of the day depends on the time-sensitivity of sessions, cost structure of the ISP, and amount of traffic not subject to time-dependent prices. Finally, we present our system integration and implementation, called TUBE, and proof-of-concept experimentation.
S. Ha, S. Sen, C. Joe-Wong, Y. Im and M. Chiang Proceedings of ACM SIGCOMM, 2012
The two largest U.S. wireless ISPs have recently moved towards usage-based pricing to better manage the growing demand on their networks. Yet usage-based pricing still requires ISPs to over-provision capacity for demand at peak times of the day. Time-dependent pricing (TDP) addresses this problem by considering when a user consumes data, in addition to how much is used. We present the architecture, implementation, and a user trial of an end-to-end TDP system called TUBE. TUBE creates a price-based feedback control loop between an ISP and its end users. On the ISP side, it computes TDP prices so as to balance the cost of congestion during peak periods with that of offering lower prices in less congested periods. On mobile devices, it provides a graphical user interface that allows users to respond to the offered prices either by themselves or using an "autopilot" mode. We conducted a pilot TUBE trial with 50 iPhone or iPad 3G data users, who were charged according to our TDP algorithms. Our results show that TDP benefits both operators and customers, flattening the temporal fluctuation of demand while allowing users to save money by choosing the time and volume of their usage.
S. Sen, C. Joe-Wong, S. Ha, J. Bawa and M. Chiang Proceedings of ACM SIGCHI, 2013
In an era of 108% annual growth in demand for mobile data and $10/GB overage fees, Internet Service Providers (ISPs) are experiencing severe congestion and in turn are hurting consumers with aggressive pricing measures. But smarter practices, such as time-dependent pricing (TDP), reward users for shifting their non-critical demand to off-peak hours and can potentially benefit both users and ISPs. Although dynamic TDP ideas have existed for many years, dynamic pricing for mobile data is only now gaining interest among ISPs. Yet TDP plans require not only systems engineering but also an understanding of economic incentives, user behavior and interface design. In particular, the HCI aspects of communicating price feedback signals from the network and the response of mobile data users need to be studied in the real world. But investigating these issues by deploying a virtual TDP data plan for real ISP customers is challenging and rarely explored. To this end, we carried out the first TDP trial for mobile data in the US with 10 families. We describe the insights gained from the trial, which can help the HCI community as well as ISPs, app developers and designers create tools that empower users to better control their usage and save on their monthly bills, while also alleviating network congestion.
L. Jiang, S. Parekh and J. Walrand Proceedings of the IEEE Network Operations and Management Symposium Workshop, 2008
The usage of a network usually differs significantly at different times of a day, due to users' time-preference. This phenomenon is also prominent in the market of "bandwidth- on-demand", since the demand is typically higher during large events. Thus, an unselfish "social planner" should deploy a proper pricing scheme to reduce congestions and achieve efficient use of the network (i.e., maximize the "social welfare"); whereas a selfish service provider (SP) can exploit the time-preference to increase its revenue. In this paper, we present a model to study the important role of time-preference in network pricing. In this model, each user chooses his access time based on his preference, the congestion level, and the price he would be charged. Without pricing, the "price of anarchy" (POA) can be arbitrarily bad. We then derive a simple pricing scheme to maximize the social welfare. Next, from the SP's viewpoint, we consider the revenue- maximizing pricing strategy and its effect on the social welfare. We show that if the SP can differentiate its prices over different users and times, the maximal revenue can be achieved, as well as the maximal social welfare. However, if the SP has insufficient information about the users and can only differentiate its prices over the access times, then the resulting social welfare can be much less than the optimum, especially when there are many low-utility users. Otherwise, the difference is bounded and less significant.
Two-sided pricing (15)
QoS-aware pricing (14)