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41
Pricing in Computer Networks: Reshaping the Research Agenda
- ACM Computer Communication Review
, 1996
"... As the Internet makes the transition from research testbed to commercial enterprise, the topic of pricing in computer networks has suddenly attracted great attention. Much of the discussion in the network design community and the popular press centers on the usage-based vs. fiat pricing debate. The ..."
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Cited by 236 (2 self)
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As the Internet makes the transition from research testbed to commercial enterprise, the topic of pricing in computer networks has suddenly attracted great attention. Much of the discussion in the network design community and the popular press centers on the usage-based vs. fiat pricing debate. The more academic literature has largely focused on devising optimal pricing policies; achieving optimal welfare requires charging marginal congestion costs for usage. In this paper we critique this optimality paradigm on three grounds: (1) marginal cost prices may not produce sufficient revenue to fully recover costs and so are perhaps of limited relevance, (2) congestion costs are inherently inaccessible to the network and so cannot reliably form the basis for pricing, and (3) there are other, more structural, goals besides optimality, and some of these goals are incompatible with the global uniformity required for optimal pricing schemes. For these reasons, we contend that the research agenda on pricing in computer network should shift away from the optimality paradigm and focus more on structural and architectural issues. Such issues include allowing local control of pricing policies,
Pricing for QoS-Enabled Networks: A Survey
, 2000
"... A complete solution to the problem of providing adequate quality of service (QoS) to heterogeneous users must take into account the issue of pricing. By adopting an appropriate pricing policy and by setting prices carefully, a service provider will be able to offer the necessary incentives for ea ..."
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Cited by 89 (1 self)
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A complete solution to the problem of providing adequate quality of service (QoS) to heterogeneous users must take into account the issue of pricing. By adopting an appropriate pricing policy and by setting prices carefully, a service provider will be able to offer the necessary incentives for each user to choose the service that best matches his/her needs, thereby discouraging over-allocation of resources and maximizing revenue and/or social welfare. In this article, we survey some of the recent research in the literature dealing with service pricing in multi-service networks. All of the work surveyed here addresses the relationship between prices and traffic management functions such as congestion control, resource provisioning, and call admission control. We summarize proposed pricing policies for the Internet and for ATM networks, as well as some studies of pricing for general QoS-enabled networks.
Economic FAQs About the Internet
- Journal of Economic Perspectives
, 1997
"... This is a set of Frequently Asked Questions (and answers) about the economic, institutional, and technological structure of the Internet. We describe the history and current state of the Internet, discuss some of the pressing economic and regulatory problems, and speculate about future developments. ..."
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Cited by 76 (4 self)
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This is a set of Frequently Asked Questions (and answers) about the economic, institutional, and technological structure of the Internet. We describe the history and current state of the Internet, discuss some of the pressing economic and regulatory problems, and speculate about future developments.
The role of responsive pricing in the Internet
- Internet Economics
, 1997
"... The Internet continues to evolve as it reaches out to a wider user population. The recent introduction of user-friendly navigation and retrieval tools for the World Wide Web has triggered an unprecedented level of interest in the Internet among the media and the general public, as well as in the tec ..."
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Cited by 42 (3 self)
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The Internet continues to evolve as it reaches out to a wider user population. The recent introduction of user-friendly navigation and retrieval tools for the World Wide Web has triggered an unprecedented level of interest in the Internet among the media and the general public, as well as in the technical community. It seems inevitable that some changes or additions are needed in the control mechanisms used to regulate access to Internet resources. In this paper, we argue that a feedback signal based on the price a user pays for network service is a workable tool to aid network operators in controlling Internet traffic. We suggest that these prices should vary dynamically based on the current utilization of network resources. We show how this responsive pricing puts control of network service back where it belongs: with the users.
A Market-Based Approach to Optimal Resource Allocation in Integrated-Services Connection-Oriented Networks
- Operations Research
, 2002
"... We present an approach to the admission control and resource allocation problem in connection-oriented networks that offer multiple services to users. Users ’ preferences are summarized by means of their utility functions, and each user is allowed to request more than one type of service. Multiple t ..."
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Cited by 32 (5 self)
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We present an approach to the admission control and resource allocation problem in connection-oriented networks that offer multiple services to users. Users ’ preferences are summarized by means of their utility functions, and each user is allowed to request more than one type of service. Multiple types of resources are allocated at each link along the path of a connection. We assume that the relation between Quality of Service (QoS) and resource allocation is given, and we incorporate it as a constraint into a static optimization problem. The objective of the optimization problem is to determine the amount of and required resources for each type of service to maximize the sum of the users ’ utilities. We prove the existence of a solution of the optimization problem and describe a competitive market economy that implements the solution and satisfies the informational constraints imposed by the nature of the decentralized resource allocation problem. The economy consists of four different types of agents: resource providers, service providers, users, and an auctioneer that regulates the prices based on the observed aggregate excess demand. The goods that are sold are: (i) the resources at each link of the network, and (ii) services constructed from these resources and then delivered to users. We specify an iterative procedure that is used by the auctioneer to update the prices, and we show that it leads to an allocation that is arbitrarily close to a solution of the optimization problem in a finite number of iterations. 1.
Paying for QoS: an optimal distributed algorithm for pricing network resources.
- In Sixth International Workshop on Quality of Service (IWQoS’98),
, 1998
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Some FAQs about Usage-Based Pricing
, 1994
"... This is a list of Frequently Asked Questions about usage-based pricing of the Internet. We argue that usage-based pricing is likely to come sooner or later and that some serious thought should be devoted to devising a sensible system of usage-based pricing. ..."
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Cited by 26 (2 self)
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This is a list of Frequently Asked Questions about usage-based pricing of the Internet. We argue that usage-based pricing is likely to come sooner or later and that some serious thought should be devoted to devising a sensible system of usage-based pricing.
Learning and implementation on the internet
- Rutgers University, Department of Economics
, 1997
"... We address the problem of learning and implementation in the Internet. When agents play repeated games in distributed environments like the Internet, they have very limited a priori information about the other players and the payo matrix. Consequently, standard solution concepts like Nash equilibria ..."
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Cited by 20 (4 self)
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We address the problem of learning and implementation in the Internet. When agents play repeated games in distributed environments like the Internet, they have very limited a priori information about the other players and the payo matrix. Consequently, standard solution concepts like Nash equilibria, or even the serially undominated set, do not apply in such a setting. To construct more appropriate solution concepts, we rst describe the essential properties that constitute \reasonable " learning behavior in distributed environments. We then study the convergence behavior of such algorithms; these results lead us to propose rather non traditional solutions concepts for this context. Finally, we discuss implementation of social choice functions with these solution concepts, and nd that only strictly coalitionally strategyproof social choice functions are implementable. 1 1
Distributed Network Flow Control Based on Dynamic Competitive Markets
- in Proceedings International Conference on Network Protocol (ICNP’98
, 1998
"... Network applications require a certain level of network performance for their proper operation. These individual guarantees can be provided if sufficient amounts of network resources are available; however, contention for the limited network resources may occur. For this reason, networks use flow co ..."
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Cited by 15 (1 self)
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Network applications require a certain level of network performance for their proper operation. These individual guarantees can be provided if sufficient amounts of network resources are available; however, contention for the limited network resources may occur. For this reason, networks use flow control to manage network resources fairly and efficiently. This paper presents a distributed microeconomic flow control technique, that models the network as competitive markets. In these markets switches price their link bandwidth based on supply and demand, and users purchase bandwidth so as to maximize their individual Quality of Service (QoS). This decentralized flow control method provides a Pareto optimal and equitable (QoSfair) bandwidth distribution. Simulation results using actual MPEG-compressed video traffic show utilization over 95% and better QoS control than max-min.
