6,834 research outputs found

    Time and volume based optimal pricing strategies for telecommunication networks

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    In the recent past, there have been several initiatives by major network providers such as Turk Telekom lead the industry towards network capacity distribution in Turkey. In this study, we use a monopoly pricing model to examine the optimal pricing strategies for “pay-per-volume” and “pay-per-time” based leasing of data networks. Traditionally, network capacity distribution includes short/long term bandwidth and/or usage time leasing. Each consumer has a choice to select volume based pricing or connection time based pricing. When customers choose connection time based pricing, their optimal behavior would be utilizing the bandwidth capacity fully therefore it can cause network to burst. Also, offering pay-per-volume scheme to the consumer provides the advantage of leasing the excess capacity for other potential customers for network provider. We examine the following issues in this study: (i) What are the extra benefits to the network provider for providing the volume based pricing scheme? and (ii) Does the amount of demand (number of customers enter the market) change? The contribution of this paper is to show that pay-per-volume is a viable alternative for a large number of customers, and that judicious pricing for pay-per-volume is profitable for the network provider

    Optimal pricing strategies for capacity leasing based on time and volume usage in telecommunication networks

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    In this study, we use a monopoly pricing model to examine the optimal pricing strategies for “pay-per-time”, “pay-per-volume” and “pay-per both time and volume” based leasing of data networks. Traditionally, network capacity distribution includes short/long term bandwidth and/or usage time leasing. Each consumer has a choice to select volume based, connection-time based or both volume and connection-time based pricing. When customers choose connection-time based pricing, their optimal behavior would be utilizing the bandwidth capacity fully, which can cause network to burst. Also, offering the pay-per-volume scheme to the consumer provides the advantage of leasing the excess capacity to other potential customers serving as network providers. However, volume-based strategies are decreasing the consumers’ interest and usage, because the optimal behaviors of the customers who choose the pay-per-volume pricing scheme generally encourages them to send only enough bytes for time-fixed tasks (for real time applications), causing quality of the task to decrease, which in turn creating an opportunity cost. Choosing pay-per time and volume hybridized pricing scheme allows customers to take advantages of both pricing strategies while decreasing (minimizing) the disadvantages of each, because consumers generally have both time-fixed and size-fixed task such as batch data transactions. However, such a complex pricing policy may confuse and frighten consumers. Therefore, in this study we examined the following two issues: (i) what (if any) are the benefits to the network provider of providing the time and volume hybridized pricing scheme? and (ii) would this offering schema make an impact on the market size? The main contribution of this study is to show that pay-per both time and volume pricing is a viable and often preferable alternative to the only time and/or only volume-based offerings for a large number of customers, and that judicious use of such pricing policy is profitable to the network provider

    Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

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    Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

    Quality of service assurance for the next generation Internet

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    The provisioning for multimedia applications has been of increasing interest among researchers and Internet Service Providers. Through the migration from resource-based to service-driven networks, it has become evident that the Internet model should be enhanced to provide support for a variety of differentiated services that match applications and customer requirements, and not stay limited under the flat best-effort service that is currently provided. In this paper, we describe and critically appraise the major achievements of the efforts to introduce Quality of Service (QoS) assurance and provisioning within the Internet model. We then propose a research path for the creation of a network services management architecture, through which we can move towards a QoS-enabled network environment, offering support for a variety of different services, based on traffic characteristics and user expectations

    Advance reservation games

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    Advance reservation (AR) services form a pillar of several branches of the economy, including transportation, lodging, dining, and, more recently, cloud computing. In this work, we use game theory to analyze a slotted AR system in which customers differ in their lead times. For each given time slot, the number of customers requesting service is a random variable following a general probability distribution. Based on statistical information, the customers decide whether or not to make an advance reservation of server resources in future slots for a fee. We prove that only two types of equilibria are possible: either none of the customers makes AR or only customers with lead time greater than some threshold make AR. Our analysis further shows that the fee that maximizes the provider’s profit may lead to other equilibria, one of which yields zero profit. In order to prevent ending up with no profit, the provider can elect to advertise a lower fee yielding a guaranteed but smaller profit. We refer to the ratio of the maximum possible profit to the maximum guaranteed profit as the price of conservatism. When the number of customers is a Poisson random variable, we prove that the price of conservatism is one in the single-server case, but can be arbitrarily high in a many-server system.CNS-1117160 - National Science Foundationhttp://people.bu.edu/staro/ACM_ToMPECS_AR.pdfAccepted manuscrip
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