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.
Pricing content delivery (7)
In modern cellular networks, content providers often pre-distribute their content on CDNs (content delivery networks) that are geographically dispersed throughout cellular networks in order to make delivering content to users more efficient. While most SDP research focuses on delivery of content from a fixed set of CDNs to users, deciding how to distribute content among CDNs has implications for both the price of setting up a CDN and the cost of delivering content to users.
Y. Song, A. Venkataramani and L. Gao Proceedings of the 2nd IEEE International Workshop on Smart Data Pricing (SDP), 2013
Content Delivery Networks (CDNs) serve a large fraction of Internet traffic today improving user-perceived response time and availability of content. With tens of CDNs competing for content producers, it is important to understand the game played by these CDNs and whether the game is sustainable in the long term. In this paper, we formulate a game-theoretic model to analyze price competition among CDNs. Under this model, we propose an optimal strategy employed by two-CDN games. The strategy is incentive-compatible since any CDN that deviates from the strategy ends up with a lower utility. The strategy is also efficient since it produces a total utility that is at least two thirds of the social optimal utility. We formally derive the sufficient conditions for such a strategy to exist, and empirically show that there exists an optimal strategy for the games with more than two CDNs.
J. Tadrous, A. Eryilmaz and H. El Gamal Proceedings of the 3rd IEEE International Workshop on Smart Data Pricing (SDP), 2014
In this work, we investigate the profit maximization problem of a network service provider through smart pricing and proactive data services. The demand characteristics of each user are dependent on the price and willingness-to-pay values of each service. By learning these characteristics, the service provider can further improve its profit performance through a proactive service of the predictable demand so as to smooth-out its load dynamics over time, and reduce the incurred cost. We formulate the joint price and proactive download allocation problem and study its impact on the expected user payments and the service provider profit. In particular, we show that proactive downloads can only enhance the expected profit of service provider and at the same time reduce the expected payments by the user, when compared with the no-proactive-service regime. The problem is studied from two perspectives: global optimization, and game theory. From the global optimization perspective, the problem is shown to be non-convex, yet an algorithm that yields a local optimal solution with better profit than the no-proactive-download scenario is developed. From the game theoretical perspective, the problem is posed as a coordination game with the user and the service provider are players. Best response dynamics are shown to converge to a Nash Equilibrium (NE) of the game, which is the local optimal solution achieved by the developed non-convex optimization algorithm.
K. Hosanagar, J. Chuang, R. Krishnan and M. D. Smith Management Science, 54(9): 1579—1593, 2008
Content delivery networks (CDNs) are a vital component of the Internet's content delivery value chain, servicing nearly a third of the Internet's most popular content sites. However, in spite of their strategic importance, little is known about the optimal pricing policies or adoption drivers of CDNs. We address these questions using analytic models of CDN pricing and adoption under Markovian traffic and extend the results to bursty traffic using numerical simulations. When traffic is Markovian, we find that CDNs should provide volume discounts to content providers. In addition, the optimal pricing policy entails lower emphasis on value-based pricing and greater emphasis on cost-based pricing as the relative density of content providers with high outsourcing costs increases. However, when traffic is bursty and content providers have varying levels of traffic burstiness, volume discounts may be suboptimal and may even be replaced by volume taxes. Finally, when there is heterogeneity in burstiness across content providers, a pricing policy that accounts for both the mean and variance in traffic such as percentile-based pricing is more profitable than traditional volume-based pricing (metering bytes delivered in a given time window). This finding is in contrast to the current practices of many CDN firms that use traditional volume-based pricing.
J. Tadrous, A. Eryilmaz and H. El Gamal Proceedings of the 2nd IEEE International Workshop on Smart Data Pricing (SDP), 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.
D. Lee, J. Mo and J. Park ACM SIGMETRICS Performance Evaluation Review, 40(2): 46—48, 2012
This paper provides an economic analysis of the ISP-operated CDN under a duopolistic competition. The two ISPs are modeled as a platform in a two-sided market providing Internet access to both content providers and consumers. By formulating a 4-level Stackelberg game, we have found that the equilibrium strategy of an ISP in determining whether to launch CDN service depends on the marginal cost of cache server deployment and the two contrary effects: "Competition Effect" and "Delay Reduction Effect."
S. Maharir, J. Ghaderi, S. Sanghavi and S. Shakkottai Proceedings of ACM SIGMETRICS, 2014
In this paper we look at content placement in the high-dimensional regime: there are n servers, and O(n) distinct types of content. Each server can store and serve O(1) types at any given time. Demands for these content types arrive, and have to be served in an online fashion; over time, there are a total of O(n) of these demands. We consider the algorithmic task of content placement: determining which types of content should be on which server at any given time, in the setting where the demand statistics (i.e. the relative popularity of each type of content) are not known a-priori, but have to be inferred from the very demands we are trying to satisfy. This is the high-dimensional regime because this scaling (everything being O(n)) prevents consistent estimation of demand statistics; it models many modern settings where large numbers of users, servers and videos/webpages interact in this way. We characterize the performance of any scheme that separates learning and placement (i.e. which use a portion of the demands to gain some estimate of the demand statistics, and then uses the same for the remaining demands), showing it is order-wise strictly suboptimal. We then study a simple adaptive scheme - which myopically attempts to store the most recently requested content on idle servers - and show it outperforms schemes that separate learning and placement. Our results also generalize to the setting where the demand statistics change with time. Overall, our results demonstrate that separating the estimation of demand, and the subsequent use of the same, is strictly suboptimal.
S.-I. Sou, P. Lin, S.-S. Chen and J.-Y. Jeng Proceedings of IEEE INFOCOM, 2012
Different from unicast data delivery through dedicated channels, next generation Multicast and Broadcast Service (MBS) delivers data with higher bandwidth efficiency through multicasting. When a group of online users receive the same MBS content, the average MBS network cost per user can be significantly reduced. However, traditional charging methods are unable to account for the variation of network resource. To address this shortfall, this paper develops an innovative concept in MBS charging to realize the goal that “the more users join an MBS session, the more everyone saves.” Specifically, we propose a multi-tariff charging method for MBS with a competitive advantage that attracts more users to subscribe to MBS services. Analysis and numerical results demonstrate that operators can alleviate customer concerns about user credit consumption with dynamic tariff while remaining profitable in MBS. These features in our multi-tariff charging method can create a “win-win” situation for both operators and subscribers.
Two-sided pricing (15)
QoS-aware pricing (14)