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.
Real-time congestion pricing (14)
Real-time congestion pricing makes prices dependent on the actual congestion in a network at a given time. The price dynamics thus serve as a congestion toll for network access: the service provider announces prices based on current congestion levels, and the user response to these prices is fed back into the control loop to compute new prices. By charging higher prices at times of greater congestion, providers can influence users to reduce their usage, thus reducing congestion.
J. Tadrous, A. Eryilmaz, and H. E. Gamal Proceedings of IEEE INFOCOM, 2013
We address the question of optimal proactive service and demand shaping for content distribution in data networks through smart pricing. We develop a proactive download scheme that utilizes the probabilistic predictability of the human demand by proactively serving potential users' future requests during the off-peak times. Thus, it smooths-out the network traffic and minimizes the time average cost of service. Moreover, we incorporate the varying economic responsiveness and demand flexibilities of users into our model to develop a demand shaping mechanism that further improves the gains of proactive downloads. To that end, we propose a model that captures the uncertainty about the users' demand as well as their responsiveness to the pricing employed by the service providers. We propose a joint proactive resource allocation and demand shaping scheme based on nonconvex optimization algorithms, and show that it always leads to strictly better performance over its proactive counterpart without demand shaping.
J. Lee, Y. Yi, S. Chong, and Y. Jin IEEE Transactions on Wireless Communications, 13(3), 1540-1554, 2014
Cellular networks are facing severe traffic overloads due to the proliferation of smart handheld devices and traffic-hungry applications. A cost-effective and practical solution is to offload cellular data through WiFi. Recent theoretical and experimental studies show that a scheme, referred to as delayed WiFi offloading, can significantly save the cellular capacity by delaying users' data and exploiting mobility and thus increasing chance of meeting WiFi APs (Access Points). Despite a huge potential of WiFi offloading in alleviating mobile data explosion, its success largely depends on the economic incentives provided to users and operators to deploy and use delayed offloading. In this paper, we study how much economic benefits can be generated due to delayed WiFi offloading, by modeling the interaction between a single provider and users based on a two-stage sequential game. We first analytically prove that WiFi offloading is economically beneficial for both the provider and users. Also, we conduct trace-driven numerical analysis to quantify the practical gain, where the increase ranges from 21% to 152% in the providers revenue, and from 73% to 319% in the users surplus.
F. M. F. Wong, C. Joe-Wong, S. Ha, Z. Liu and M. Chiang IEEE/ACM International Symposium on Quality of Service
Recent increases in network traffic have led to severe congestion in broadband networks. We propose to mitigate this problem with a two-level edge-based solution that incentivizes users to moderate their bandwidth usage based on their actual needs. In the first level, home gateways are given QoE (quality of experience) credits that they can spend to receive more bandwidth at congested times; to ensure fairness, the credits are redistributed to other gateways after they are spent. We show that this scheme guarantees long-term fairness and maximizes users' total satisfaction at the equilibrium. In the second level, each gateway allocates bandwidth among its users and apps according to its own priorities. Gateways can thus customize their bandwidth allocation depending on individual preferences. We develop a prototype of this second-level allocation on commodity wireless routers. We then consider an example scenario and show by simulation and implementation results that our solution outperforms an equal bandwidth allocation, increasing users' overall utility and fairly allocating bandwidth across users.
A. Gupta, D. O. Stahl, and A. B. Whinston L. W. McKnight and J. P. Bailey, Eds., Internet Economics, MIT Press, Cambridge, MA, 323-352, 1997
In the near future a collection of data communication networks (the Internet) is going to provide a variety of services through multiple service classes where each service class will provide a different performance in terms of response time. These service classes will be designed to provide appropriate levels of service to user applications. This paper presents a priority pricing scheme which can be used to manage such a network. Each priority class may be mapped to one or more service classes. The approach presented in this paper can be implemented in a completely decentralized environment. Some simulation results using a hypothetical network with different service levels and requirements are also presented. These results indicate that priority pricing improves the performance significantly as compared to free access (or no usage base pricing) and flat pricing. The implementation of this priority pricing scheme is a practical solution to incentive compatibility in a network with diverse and unobservable user characteristics.
F. Kelly, A. K. Maulloo and D. H. K. Tan 49(3): 237-252,1998
This paper analyses the stability and fairness of two classes of rate control algorithm for communication networks. The algorithms provide natural generalisations to large-scale networks of simple additive increase/multiplicative decrease schemes, and are shown to be stable about a system optimum characterised by a proportional fairness criterion. Stability is established by showing that, with an appropriate formulation of the overall optimisation problem, the network's implicit objective function provides a Lyapunov function for the dynamical system defined by the rate control algorithm. The network's optimisation problem may be cast in primal or dual form: this leads naturally to two classes of algorithm, which may be interpreted in terms of either congestion indication feedback signals or explicit rates based on shadow prices. Both classes of algorithm may be generalised to include routing control, and provide natural implementations of proportionally fair pricing.
F. Kelly Operations Research Letters, 15(1): 1-9, 1994
We propose a tariff structure for high speed multiservice networks which encourages the cooperative sharing of information between users and the network. In the case of on/off sources with a policed peak rate the tariff structure takes a very simple form: a charge am per unit time and a charge bm per cell carried, where the pair (am, bm) are fixed by a declaration m, made by the user at the time of call admission, of the expected rate of the source.
J. Murphy, L. Murphy and E. C. Posner Proceedings of the International Teletraffic Conference, 1994
This paper addresses the problem of bandwidth allocation in ATM networks. Users are assumed to place a benet on the bandwidth they are assigned by the network. The network constraints are used to derive prices for the bandwidths which induce users to share the limited network resources according to their benet functions. This formulation leads to a distributed pricing algorithm, which is integrated with the problem of assigning capacities on the virtual paths connecting ATM switches such that the physical trunk capacity constraints are satised. The algorithm can be used for trac management in a Virtual Private Network (VPN) in which one entity controls all the users.
J. Murphy and L. Murphy IFIP Transactions C: Communications Systems, C-24, 333-351, 1994
Admission control and bandwidth allocation are important issues in telecommunica- tions networks, especially when there are random uctuating demands for service and variations in the service rates. In the emerging broadband communications environment these services are likely to be oered via an ATM network. In order to make ATM future safe, methods for controlling the network should not be based on the characteristics of present services. We propose one bandwidth allocation method which has this property. Our proposed approach is based on pricing bandwidth to re ect network utilization, with users competing for resources according to their individual bandwidth valuations. The prices may be components of an actual tari or they may be used as control signals, as in a private network. Simulation results show the improvement possible with our scheme versus a leaky bucket method in terms of cell loss probability, and conrm that a small queue with pricing can be ecient to multiplex heterogeneous sources.
J. MacKie-Mason and H. Varian B. Kahin and J. Keller, Eds., Public Access to the Internet. Prentice Hall, Englewood Cliffs, NJ, 1995
This paper was prepared for the conference ``Public Access to the Internet,'' JFK School of Government, May 26--27 , 1993. We describe some of the technology and costs relevant to pricing access to and usage of the Internet, and discuss the components of an efficient pricing structure. We suggest a possible smart-market mechanism for pricing traffic on the Internet.
A. Ganesh, K. Laevens and R. Steinberg Proceedings of IEEE INFOCOM, 2001
The problem of sharing bandwidth in a communication network has been the focus of much research aimed at guaranteeing an appropriate quality of service to users. This is particularly challenging in an environment with a great diversity of users and applications, which makes it difficult, if not impossible, to tightly constrain user attributes and requirements. This motivates shifting the burden of rate allocation from the network to the end-systems. We propose a decentralized scheme for user adaptation and study its dynamics. The proposed scheme uses congestion prices as a mechanism for providing both feedback and incentives to end-systems
I. C. Paschalidis and J. N. Tsitsikilis IEEE/ACM Transactions on Networking, 8(2):171-184, 2000
We consider a service provider (SP) who provides access to a communication network or some other form of on-line services. Users initiate calls that belong to a set of diverse service classes, differing in resource requirements, demand pattern, and call duration. The SP charges a fee per call, which can depend on the current congestion level, and which affects users' demand for calls. We provide a dynamic programming formulation of the problems of revenue and welfare maximization, and derive some qualitative properties of the optimal solution. We also provide a number of approximate approaches, together with an analysis that indicates that near-optimality is obtained for the case of many, relatively small, users. In particular, we show analytically as well as computationally, that the performance of an optimal pricing strategy is closely matched by a suitably chosen static price, which does not depend on instantaneous congestion. This indicates that the easily implementable time-of-day pricing will often suffice. Throughout, we compare the alternative formulations involving revenue or welfare maximization, respectively, and draw some qualitative conclusions
H. Yaiche, R. R. Mazumdar and C. Rosenberg IEEE/ACM Transactions on Networking, 8(5):667-678, 2000
An abstract is not available.
F. Fund, S. A. Hosseini and S. S. Panwar Proceedings of the 3rd IEEE International Workshop on Smart Data Pricing (SDP), 2014
A great deal of research energy has been focused on the challenge of delivering high-quality video content to mobile users. In many over-the-top video services, however, the scheduler responsible for channel resource allocation is not aware of content characteristics or playback schedules at end user devices. Therefore, it cannot allocate physical resources in a way that maximizes video quality. For example, it cannot prioritize the transmission of a video frame that is to be displayed within seconds over one whose playback deadline is minutes away. Furthermore, for content that is to be viewed immediately, previous pricing structures that incentivize delaying network use to off-peak hours or WiFi offloading do not apply. To address this issue, we introduce a tiered link quality-dependent data pricing scheme for use together with usage-based pricing in wireless networks. Our pricing model encourages selfish users to prefetch video content during short intervals of good link quality, and use minimal resources when they have a poor link quality. This offers an economic incentive to video consumers to use physical resources more efficiently even with an oblivious scheduler, and leads to better overall video quality for all users in a wireless cell, as well as increased revenue for the wireless service provider.
D. Niu and B. Li Proceedings of the 3rd IEEE International Workshop on Smart Data Pricing (SDP), 2014
Media webcasting and conferencing that involve many geographically distributed participants contribute significantly to congestion in the Internet. The current usage-based data pricing model does not take into account the hidden cost imposed by media streaming in the Internet core, including the network cost of replicating and relaying traffic in video multicast, and could potentially exacerbate congestion. In lieu of the recently emerged content sponsoring, in this paper, we present a simple congestion pricing model for ISPs (e.g. Comcast) to charge media streaming operators (e.g. Netflix) based on the bandwidth-delay product on each overlay link (either server-to-server or server-to-user) that the media streaming operator has chosen to use. The proposed pricing policy incentivizes different media streaming applications to collectively reduce their “waiting packets” in the Internet, alleviating congestion. We formulate the min-cost single and multiple multicast problems for the applications to construct their streaming overlays, based on a dense pool of CDN nodes. An efficient EM algorithm is given to solve the proposed geometric optimization problem and is evaluated through simulations.
Two-sided pricing (15)
QoS-aware pricing (14)