Charging and rate control for elastic traffic

Kelly, F. P.

doi:10.1002/ett.4460080106

Cited by 2,497 publications

(1,904 citation statements)

References 9 publications

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“…, B n , and we need to allocate D clicks, with no ceiling on the per-click price. Here we apply a simple rule known as proportional sharingProportional sharing (see [26,25] Greedy First-Price Mechanism. A natural mechanism for the general single-slot case is to solve the associated "fractional knapsack"Fractional knapsack problem problem, and charge bidders their bid; i.e., starting with the highest bidder, greedily add bidders to the allocation, charging them their bid, until all the clicks are allocated.…”

Section: Special Case: One Slotmentioning

confidence: 99%

Algorithmic Methods for Sponsored Search Advertising

Feldman,

Muthukrishnan

2008

Performance Modeling and Engineering

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Modern commercial Internet search engines display advertisements along side the search results in response to user queries. Such sponsored search relies on market mechanisms to elicit prices for these advertisements, making use of an auction among advertisers who bid in order to have their ads shown for specific keywords. We present an overview of the current systems for such auctions and also describe the underlying game-theoretic aspects. The game involves three partiesadvertisers, the search engine, and search users-and we present example research directions that emphasize the role of each. The algorithms for bidding and pricing in these games use techniques from three mathematical areas: mechanism design, optimization, and statistical estimation. Finally, we present some challenges in sponsored search advertising.

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Section: Special Case: One Slotmentioning

confidence: 99%

Algorithmic Methods for Sponsored Search Advertising

Feldman,

Muthukrishnan

2008

Performance Modeling and Engineering

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“…If the transmission rate can be supported, then the lower tier ISP i obtains a utility of A ij (x ij ) where A ij is a strictly concave function in x ij . As indicated in [9], concave function is commonly used to represent elastic traffic, which is the dominant traffic in the Internet. In here, a weighted log function is used such that A ij (x ij ) = w ij log(1 + x ij ).…”

Section: Network and Game-theoretic Modelsmentioning

confidence: 99%

On the Interaction and Competition among Internet Service Providers

Lee

Lui

2008

IEEE J. Select. Areas Commun.

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The current Internet architecture comprises of different privately owned Internet service providers (ISPs) where higher tier ISPs supply connectivity service to lower tier ISPs and charge these ISPs for the transit service. For the higher tier ISPs, the main concern is how to increase the profit by attracting more lower tier ISPs (or traffic), while the lower tier ISPs concern about the connectivity, quality of service as well as the cost of the transit service. In this work, we seek to understand the interaction between different tiers of ISPs. Note that the lower tier ISPs can transmit traffic to each other, either by purchasing the service from higher tier ISPs, or by setting "private peering links" between themselves. Higher tier ISPs, on the other hand, cannot charge the transit service at will since there is competition among higher tier ISPs. We model the interaction of these ISPs via a game theoretic approach. We study the issues of (a) impact of private peering relationship among the lower tier ISPs, (b) under a competitive market, how can the higher tier ISPs perform resource allocation and revenue maximization so that resource monopoly can be avoided, and (c) conditions wherein higher tier ISPs are willing to perform network upgrade, in particular, when we scale up the network. Our mathematical framework provides insights on the interaction among ISPs and shows these ISPs can still gain profits as they upgrade the network infrastructures. Extensive simulations are carried out to quantify and support our theoretical claims.

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“…This weight may reflect the price charged to the traffic class per unit time of usage [10,11] and is used in the computation of credits in order to provide differentiated services among the traffic classes.…”

Section: A Simple Algorithm For Time-invariant Channelsmentioning

confidence: 99%

<title>Class and channel condition-based scheduler for EDGE/GPRS</title>

Agrawal

Bedekar

et al. 2001

SPIE Proceedings

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The efficient management of the radio resource of a 3-G system is important from an operator's perspective. This, however, cannot be the only concern when quality of service (QoS) negotiations have been made for various users and the operator has to uphold these. This leads to a fairness objective that the operator has to keep in mind. In this paper we outline a scheme to perform packet-level scheduling and resource allocation at the wireless node that takes into account the notions of both efficiency and fairness and presents a means to explore the trade-off between these two notions. As a part of this scheme we see the scheduling problem as deciding not just the packet transmission schedule but also the power allocation, the modulation and coding scheme allocation and the spreading code determination since the latter three directly influence the radio resources consumed. Using a utility maximization formulation based on the data-rates that the mobiles can transmit at, we decide on the weights for a weighted proportionally fair allocation based scheduling algorithm. We also show how one can adapt the weights and the algorithm for a time-varying channel. We conclude with a simulation based performance analysis for infinitelybacklogged sources and TCP sources on an EDGE system.

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Charging and rate control for elastic traffic

Cited by 2,497 publications

References 9 publications

Algorithmic Methods for Sponsored Search Advertising

Algorithmic Methods for Sponsored Search Advertising

On the Interaction and Competition among Internet Service Providers

<title>Class and channel condition-based scheduler for EDGE/GPRS</title>

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