Traditional regulatory methods for spectrum licensing have been recently identified as one of the causes for the under-utilization of the valuable radio spectrum. Governmental agencies such as the Federal Communications Commission (FCC) are seeking ways to remove stringent regulatory barriers and facilitate broader access to the spectrum resources. The goal is to allow for an improved and ubiquitous sharing of the precious radio spectrum between commercial service providers. In this paper, we propose a novel noncooperative game theoretic approach, to show how to foster more sharing of the radio spectrum via the use of regulatory power. We define a two stage game in which the government regulators move first, followed by the providers. The providers are incentivized by lower spectrum allocation fees from the regulators in return for proof-of-sharing. The providers are offered discounted spectrum bands, potentially at different locations, but will be asked to provide coverage to users that are not subscribed to them so as to maintain their subsidy incentives from the government. In a simplification of the model, analytical expressions for the providers' perfect equilibrium strategies are derived, and we argue for the existence of the government's part of a perfect equilibrium. Our analysis shows that through subsidization, the government can provide small service providers a fair chance to compete with the large providers, thereby avoiding monopolization in the market.
Only recently, Yu and Kim @cite_34 focused on the concept of using subsidies for spectrum management. Their work analyzed price and quality of service (QoS) subsidy schemes to increase the utility of the consumers from the data plans. The goal is to increase the availability of wireless data plans to more users. In their model, the regulator offers a subsidized (i.e., less expensive) data plan to the end users with lower quality of service via subsidization to the providers. In our work, we consider subsidizing the spectrum to the providers and focus on implementing a subsidy system mainly between the providers and the government. We aim to make the subsidization seamless to the end users and aim to incentivize the providers to be more welcoming to the users subscribed to other providers. As a major difference, we focus on spectrum subsidy rather than data plan subsidy.
@cite_34
2020629327
Due to the emergence of smart devices, mobile data traffic grows exponentially. A Cisco report predicts that global mobile data traffic will increase 26-fold between 2010 and 2015. Therefore, the spectrum shortage continues and the spectrum price increases, which will eventually lead to decrease of user welfare. Another side effect of the data traffic growth is the polarization of data traffic among users. To resolve these problems, we introduce two subsidy schemes (i.e., price and quality of service (QoS) subsidy schemes) and mathematically analyze the effect of each scheme. We identify that if the regulator has sufficient spectrum amount for the network service, then the QoS subsidy scheme will be a good choice for all players in the network service market. Otherwise, the price subsidy scheme can be better from user welfare perspective. Even though our analytic results are derived under some assumptions for mathematical tractability, it will provide good intuitions for spectrum regulation. Unlike telephone operators, which pay termination fees to reach the users of another network, Internet Content Providers (CPs) do not pay the Internet Service Providers (ISPs) of users they reach. While the consequent cross subsidization to CPs has nurtured content innovations at the edge of the Internet, it reduces the investment incentives for the access ISPs to expand capacity. As potential charges for terminating CPs' traffic are criticized under the net neutrality debate, we propose to allow CPs to voluntarily subsidize the usagebased fees induced by their content traffic for end-users. We model the regulated subsidization competition among CPs under a neutral network and show how deregulation of subsidization could increase an access ISP's utilization and revenue, strengthening its investment incentives. Although the competition might harm certain CPs, we find that the main cause comes from high access prices rather than the existence of subsidization. Our results suggest that subsidization competition will increase the competitiveness and welfare of the Internet content market; however, regulators might need to regulate access prices if the access ISP market is not competitive enough. We envision that subsidization competition could become a viable model for the future Internet. We address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maximize its profit under quality of service (QoS) constraint for primary users. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. The QoS degradation of the primary services is considered as the cost in offering spectrum access to the secondary users. For the secondary users, we adopt a utility function to obtain the demand function. With a Bertrand game model, we analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., equilibrium pricing adopted by the primary services). We present distributed algorithms to obtain the solution for this dynamic game. The stability of the proposed dynamic game algorithms in terms of convergence to the Nash equilibrium is studied. However, the Nash equilibrium is not efficient in the sense that the total profit of the primary service providers is not maximized. An optimal solution to gain the highest total profit can be obtained. A collusion can be established among the primary services so that they gain higher profit than that for the Nash equilibrium. However, since one or more of the primary service providers may deviate from the optimal solution, a punishment mechanism may be applied to the deviating primary service provider. A repeated game among primary service providers is formulated to show that the collusion can be maintained if all of the primary service providers are aware of this punishment mechanism, and therefore, properly weight their profits to be obtained in the future. The interaction of a content provider with end users on an infrastructure platform built and maintained by a service provider can be viewed as a two-sided market. Content sponsoring, i.e., charging the content provider instead of viewers for resources consumed in viewing the content, can benefit all parties involved. Without being charged directly or having it counted against their monthly data quotas, end users will view more content, allowing the content provider to generate more advertising revenue, extracted by the service provider to subsidize its investment and operation of the network infrastructure. However, realizing such gains requires a proper contractual relationship between the service provider and content provider. We consider the determination of this contract through a Stackelberg game. The service provider sets a pricing schedule for sponsoring and the content provider responds by deciding how much content to sponsor. We analyze the best strategies for the content provider and service provider in the event that the underlying demand for the content is uncertain. Two separate settings are defined. In the first, end users can be charged for non-sponsored views on a per-byte basis. In the second we extend the model to the more common case in which end users purchase data quotas on a periodic basis. Our main conclusion is that a coordinating contract can be designed that maximizes total system profit. Moreover, the additional profit due to sponsoring can be split between the content provider and service provider in an arbitrary manner. Dynamic spectrum access can significantly improve the spectrum utilization. For wireless service providers, the emergence of dynamic spectrum access brings new opportunities and challenges. The flexible spectrum acquisition gives a particular provider the chance to easily adapt its system capacity to fit end users' demand. However, the competition among several providers for both spectrum and end users complicates the situation. In this paper, we propose a general three-layer spectrum market model for the future dynamic spectrum access system, in which the interaction among spectrum holder, wireless service providers and end users are considered. We study a duopoly situation, where two wireless service providers participate in bandwidth competition in spectrum purchasing and price competition to attract end users, with the aim of maximizing their own profit. We believe we are the first one to explicitly study the relation of these two competitions in dynamic spectrum market. We formulate the wireless service providers' competition as a non-cooperative two-stage game. We first analyze the static game when full information is available for providers. Under general assumptions about the price and demand functions, a unique pure Nash equilibrium is identified as the outcome of the game, which shows the stability of the market. We further evaluate the market efficiency of the equilibrium in a symmetric case, and show that the gap with the social optimal is bounded within a small constant ratio. When the market information is limited, we provide myopically optimal adjustment algorithms for the providers. With such strategies, short term price updating converges to the Nash equilibrium of the given subgame, while long term bandwidth updating converges to a point close to the Nash equilibrium of the full game. Data traffic demand over the Internet is increasing rapidly, and it is changing the pricing model between Internet service providers (ISPs), content providers (CPs) and end users. One recent pricing proposal is sponsored data plan, i.e., when accessing contents from a particular CP, end users do not need to pay for that volume of traffic consumed, but the CP will sponsor for this data consumption. In this paper, our goal is to understand the rationale behind this new pricing model, as well as its impacts to the wireless data market, in particular, who will benefit and who will be hurt from this scheme. 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 include: 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 un- balance in revenue distribution between different CPs; CPs with higher unit income and poorer technology support are more likely to prefer the sponsored data plan. We consider a cost sharing system where users are selfish and act according to their own interest. There is a set of facilities and each facility provides services to a subset of the users. Each user is interested in purchasing a service, and will buy it from the facility offering it at the lowest cost. The overall system performance is defined to be the total cost of the facilities chosen by the users. A central authority can encourage the purchase of services by offering subsidies that reduce their price, in order to improve the system performance. The subsidies are financed by taxes collected from the users. Specifically, we investigate a non-cooperative game, where users join the system, and act according to their best response. We model the system as an instance of a set cover game, where each element is interested in selecting a cover minimizing its payment. The subsidies are updated dynamically, following the selfish moves of the elements and the taxes collected due to their payments. Our objective is to design a dynamic subsidy mechanism that improves on the overall system performance while collecting as taxes only a small fraction of the sum of the payments of the users. The performance of such a subsidy mechanism is thus defined by two different quality parameters: (i) the price of anarchy, defined as the ratio between the cost of the Nash equilibrium obtained and the cost of an optimal solution; and (ii) the taxation ratio, defined as the fraction of payments collected as taxes from the users. We investigate two different models: (i) an integral model in which each element is covered by a single set; and (ii) a fractional model in which an element can be fractionally covered by several sets. Let f denote the maximum number of sets that an element can belong to. For the fractional model, we provide a subsidy mechanism such that, for any e≤1, the price of anarchy is @math and the taxation ratio is e. For the integral model, we provide a subsidy mechanism such that, for any e≤1, the price of anarchy is @math and the taxation ratio is e, where n is the number of elements. Pricing content-providers for connectivity to end- users and setting connection parameters based on the price is an evolving model on the Internet. The implications are heavily debated in telecom policy circles, and some advocates of "Network Neutrality" have opposed price based differentiation in connectivity. However, pricing content providers can possibly subsidize the end-user's cost of connectivity, and the consequent increase in end-user demand can benefit ISPs and content providers. This paper provides a framework to quantify the precise trade-off in the distribution of benefits among ISPs, content-providers, and end-users. The framework generalizes the well-known utility maximization based rate allocation model, which has been extensively studied as an interplay between the ISP and the end-users, to incorporate pricing of content-providers. We derive the resulting equilibrium prices and data rates in two different ISP market conditions: competition and monopoly. Network neutrality based restriction on content-provider pricing is then modeled as a constraint on the maximum price that can be charged to content-providers. We demonstrate that, in addition to gains in total and end- user surplus, content-provider experiences a net surplus from participation in rate allocation under low cost of connectivity. The surplus gains are, however, limited under monopoly conditions in comparison to competition in the ISP market. We consider the problem of spectrum trading with multiple licensed users (i.e., primary users) selling spectrum opportunities to multiple unlicensed users (i.e., secondary users). The secondary users can adapt the spectrum buying behavior (i.e., evolve) by observing the variations in price and quality of spectrum offered by the different primary users or primary service providers. The primary users or primary service providers can adjust their behavior in selling the spectrum opportunities to secondary users to achieve the highest utility. In this paper, we model the evolution and the dynamic behavior of secondary users using the theory of evolutionary game. An algorithm for the implementation of the evolution process of a secondary user is also presented. To model the competition among the primary users, a noncooperative game is formulated where the Nash equilibrium is considered as the solution (in terms of size of offered spectrum to the secondary users and spectrum price). For a primary user, an iterative algorithm for strategy adaptation to achieve the solution is presented. The proposed game-theoretic framework for modeling the interactions among multiple primary users (or service providers) and multiple secondary users is used to investigate network dynamics under different system parameter settings and under system perturbation. The success of the Internet is remarkable in light of the decentralized manner in which it is designed and operated. Unlike small scale networks, the Internet is built and controlled by a large number of disperate service providers who are not interested in any global optimization. Instead, providers simply seek to maximize their own profit by charging users for access to their service. Users themselves also behave selfishly, optimizing over price and quality of service. Game theory provides a natural framework for the study of such a situation. However, recent work in this area tends to focus on either the service providers or the network users, but not both. This paper introduces a new model for exploring the interaction of these two elements, in which network managers compete for users via prices and the quality of service provided. We study the extent to which competition between service providers hurts the overall social utility of the system. The success of the Internet is remarkable in light of the decentralized manner in which it is designed and operated. Unlike small scale networks, the Internet is built and controlled by a large number of disparate service providers who are not interested in any global optimization. Instead, providers simply seek to maximize their own profit by charging users for access to their service. Users themselves also behave selfishly, optimizing over price and quality of service. Game theory provides a natural framework for the study of such a situation. However, recent work in this area tends to focus on either the service providers or the network users, but not both. This paper introduces a new model for exploring the interaction of these two elements, in which network managers compete for users via prices and the quality of service provided. We study the extent to which competition between service providers hurts the overall social utility of the system. In the future, we can expect to see more dynamic service offerings and profiles, as users move from long-term service provider agreements to more opportunistic service models. Moreover, when the radio spectrum is itself traded in a market- based scenario, wireless service providers (WSPs) will likely require new strategies to deploy services, define service profiles, and price them. Currently, there is little understanding on how such a dynamic trading system will operate so as to make the system feasible under economic terms. From an economic point of view, we analyze two main components of this overall trading system: (i) spectrum allocation to WSPs and (ii) interaction of end users with the WSPs. For this two-tier trading system, we present a winner determining sealed-bid knapsack auction mechanism that dynamically allocates spectrum to the WSPs based on their bids. We propose a dynamic pricing strategy based on game theory to capture the conflict of interest between WSPs and end users, both of whom try to maximize their respective net utilities. We show that even in such a greedy and non-cooperative behavioral game model, it is in the best interest of the WSPs to adhere to a price threshold which is a consequence of a price equilibrium in an oligopoly situation. Through simulation results, we show that the proposed auction entices the WSPs to participate in the auction, makes optimal use of the common spectrum pool, and avoids collusion among WSPs. Moreover, numerical results demonstrate how pricing can be used as an effective tool for providing incentives to the WSPs to upgrade their network resources and offer better services. Wireless network virtualization is emerging as an important technology for next-generation (5G) wireless networks. A key advantage of introducing virtualization in cellular networks is that service providers can robustly share virtualized network resources (e.g., infrastructure and spectrum) to extend coverage, increase capacity, and reduce costs. However, the inherent features of wireless networks, i.e., the uncertainty in user equipment (UE) locations and channel conditions impose significant challenges on virtualization and sharing of the network resources. In this context, we propose a stochastic optimization-based virtualization framework that enables robust sharing of network resources. Our proposed scheme aims at probabilistically guaranteeing UEs' Quality of Service (QoS) demand satisfaction, while minimizing the cost for service providers, with reasonable computational complexity and affordable network overhead. To provide seamless mobility with high-speed wireless connectivity, future generation wireless networks must support heterogeneous wireless access. Pricing schemes adopted by different service providers is crucial and will impact the decisions of users in selecting a network. In this article, we provide a comprehensive survey of the issues related to pricing in heterogeneous wireless networks and possible approaches to the solution of the pricing problem. First, we review the related work on pricing for homogeneous wireless networks in which a single wireless technology is available to the users. Then, we outline the major issues in designing resource allocation and pricing in heterogeneous wireless access networks. To this end, we propose two oligopolistic models for price competition among service providers in a heterogeneous wireless environment consisting of WiMax and WiFi access networks. A non-cooperative game is formulated to obtain the price for the service providers. Two different equilibria, namely, the Nash and the Stackelberg equilibria are considered as the solutions of the simultaneous-play and leader-follower price competitions, respectively. Cloud providers have begun to allow users to bid for surplus servers on a spot market. These servers are allocated if a user's bid price is higher than their market price and revoked otherwise. Thus, analyzing price data to derive optimal bidding strategies has become a popular research topic. In this paper, we argue that sophisticated bidding strategies, in practice, do not provide any advantages over simple strategies for multiple reasons. First, due to price characteristics, there are a wide range of bid prices that yield the optimal cost and availability. Second, given the large number of spot markets, there is always a market with available surplus resources. Thus, if resources become unavailable due to a price spike, users need not wait until the spike subsides, but can instead provision a new spot resource elsewhere and migrate to it. Third, current spot market rules enable users to place maximum bids for resources without any penalty. Given bidding's irrelevance, users can adopt trivial bidding strategies and focus instead on modifying applications to efficiently seek out and migrate to the lowest cost resources. Cloud computing is an upcoming and promising solution for utility computing that provides resources on demand. As it has grown into a business model, a large number of cloud service providers exist today in the cloud market, which further is expanding exponentially. Many cloud service providers, with almost similar functionality, pose a selection problem to the cloud users. To assist the users in the best service selection, as per its requirement, a framework has been developed in which users list their quality of service QoS expectation, while service providers express their offerings. Experience of the existing cloud users is also taken into account in order to select the best cloud service provider. This work identifies some new QoS metrics, besides few existing ones, and defines it in a way that eases both the user and the provider to express their expectations and offers, respectively, in a quantified manner. Further, a dynamic and flexible model, using a variant of ranked voting method, is proposed that considers users' requirement and suggests the best cloud service provider. Case studies affirm the correctness and the effectiveness of the proposed model. Copyright © 2016 John Wiley & Sons, Ltd. As Internet traffic grows exponentially due to the pervasive Internet accesses via mobile devices and increasing adoptions of cloud-based applications, broadband providers start to shift from flat-rate to usage-based pricing, which has gained support from regulators such as the FCC. We consider generic congestion-prone network services and study usage-based pricing of service providers under market competition. Based on a novel model that captures users' preferences over price and congestion alternatives, we derive the induced congestion and market share of the service providers under a market equilibrium and design algorithms to calculate them. By analyzing different market structures, we reveal how users' value on usage and sensitivity to congestion influence the optimal price, revenue, and competition of service providers, as well as the social welfare. We also obtain the conditions under which monopolistic providers have strong incentives to implement service differentiation via Paris Metro Pricing and whether regulators should encourage such practices. As Internet traffic grows exponentially due to the pervasive Internet accesses via mobile devices and increasing adoptions of cloud-based applications, broadband providers start to shift from flat-rate to usage-based pricing, which has gained support from regulators such as the FCC. We consider generic congestion-prone network services, including cloud services, and study the pay-as-you-go type of usage-based pricing of service providers under market competition. Based on a novel model that captures users' preferences over usage price and congestion alternatives, we derive the induced congestion and market share of the service providers under a market equilibrium and design algorithms to calculate them. By analyzing different market structures, we reveal how users' value on usage and sensitivity to congestion influence the optimal price, revenue, and competition of service providers, as well as the social welfare. We also obtain the conditions under which monopolistic providers have strong incentives to implement service differentiation via Paris Metro Pricing and whether regulators should encourage such practices. There is a growing adoption of cloud computing services, attracting users with different requirements and budgets to run their applications in cloud infrastructures. In order to match users' needs, cloud providers can offer multiple service classes with different pricing and Service Level Objective (SLO) guarantees. Admission control mechanisms can help providers to meet target SLOs by limiting the demand at peak periods. This paper proposes a prediction-based admission control model for IaaS clouds with multiple service classes, aiming to maximize request admission rates while fulfilling availability SLOs defined for each class. We evaluate our approach with trace-driven simulations fed with data from production systems. Our results show that admission control can reduce SLO violations significantly, specially in underprovisioned scenarios. Moreover, our predictive heuristics are less sensitive to different capacity planning and SLO decisions, as they fulfill availability SLOs for more than 91 of requests even in the worst case scenario, for which only 56 of SLOs are fulfilled by a simpler greedy heuristic and as little as 0.2 when admission control is not used. This paper addresses the behavior of the selfish service providers in the form of IP sinks providing high-speed IP access. Service providers compete for mobile users by adjusting the price they charge for their services. Their aim is to maximize the total collected profit. Mobile users are also selfish choosing the service provider offering the best quality of service and price combination. As the service providers come closer to each other, we show the existence of three critical phase transitions in their behavior. Depending on the separation between them, there may exists a unique Nash equilibrium, or a continuum of Nash equilibria, or no Nash equilibrium. We completely characterize the pricing strategies of service providers at Nash equilibria. We also prove that the total social welfare in the presence of selfish providers is close to the maximum social welfare that can reached through non- selfish optimization.