Optimal Auctions for Multi-Unit Procurement with Volume Discount Bids

Gautam, R.K.; Hemachandra, N.; Narahari, Y.; Prakash, Hastagiri

doi:10.1109/cec-eee.2007.76

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…In our model, both cost and QoS are correlated. Hence, the design of an optimal auction is not trivial [19], [60].…”

Section: Cloud-optimal Mechanismmentioning

confidence: 99%

“…Myerson [22] defines a virtual parameter for ranking the buyers. This virtual parameter is modified [12], [19], [60] to capture the correlation between cost and quantity, and is called virtual cost.…”

Section: Cloud-optimal Mechanismmentioning

confidence: 99%

“…This multidimensional attribute of cloud vendors makes this a nontrivial problem. In particular, we cannot apply traditional optimizing schemes in this case [12], [19], [60].…”

Section: Definition 43 the Virtual Cost Is Defined Asmentioning

confidence: 99%

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A Mechanism Design Approach to Resource Procurement in Cloud Computing

Prasad¹,

Rao

2014

IEEE Trans. Comput.

131

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We present a cloud resource procurement approach which not only automates the selection of an appropriate cloud vendor but also implements dynamic pricing. Three possible mechanisms are suggested for cloud resource procurement: cloud-dominant strategy incentive compatible (C-DSIC), cloud-Bayesian incentive compatible (C-BIC), and cloud optimal (C-OPT). C-DSIC is dominant strategy incentive compatible, based on the VCG mechanism, and is a low-bid Vickrey auction. C-BIC is Bayesian incentive compatible, which achieves budget balance. C-BIC does not satisfy individual rationality. In C-DSIC and C-BIC, the cloud vendor who charges the lowest cost per unit QoS is declared the winner. In C-OPT, the cloud vendor with the least virtual cost is declared the winner. C-OPT overcomes the limitations of both C-DSIC and C-BIC. C-OPT is not only Bayesian incentive compatible, but also individually rational. Our experiments indicate that the resource procurement cost decreases with increase in number of cloud vendors irrespective of the mechanisms. We also propose a procurement module for a cloud broker which can implement C-DSIC, C-BIC, or C-OPT to perform resource procurement in a cloud computing context. A cloud broker with such a procurement module enables users to automate the choice of a cloud vendor among many with diverse offerings, and is also an essential first step toward implementing dynamic pricing in the cloud.Index Terms-Cloud computing, mechanism design, cloud broker, resource procurement, reverse auctions, multiattribute auctions, dynamic pricing Ç 1 INTRODUCTION C LOUD computing is an increasingly popular paradigm of offering services over the Internet [1]. It is also an active area of research, and the popularity of this paradigm is growing rapidly. Many companies like Amazon, IBM, Google, salesforce.com, Unisys, and so on, now offer cloud services. The main advantage of cloud computing is the ability to provision IT resources on demand (thus avoiding the problems of over-provisioning and under-provisioning which are commonly seen with organizations that have widely variable requirements due to growth/shrinkage, seasonal peaks, and valleys, etc.). The resources offered may include storage, CPU processing power, IT services, and so on. These resources are often geographically distant from users.We can say the following:. A cloud user is a person or an organization (such as an SME-small and medium enterprise) that uses cloud services. . A cloud vendor is an organization that offers cloud services for use on payment. . A cloud broker [2] is a middleware that interacts with service providers on behalf of the user. It is responsible for configuring the user's settings suitably and for procuring resources.

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“…In our model, both cost and QoS are correlated. Hence, the design of an optimal auction is not trivial [19], [60].…”

Section: Cloud-optimal Mechanismmentioning

confidence: 99%

Section: Cloud-optimal Mechanismmentioning

confidence: 99%

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A Mechanism Design Approach to Resource Procurement in Cloud Computing

Prasad¹,

Rao

2014

IEEE Trans. Comput.

131

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“…In his seminal work, Myerson [14] characterized an optimal auction for selling a single unit of a single item. Extending his work has been attempted by several researchers and there have been some generalizations of his work for multi-unit single item auctions [11,9,7]. Armstrong [1] characterized an optimal auction for two objects where type sets are binary.…”

Section: Contributions and Outlinementioning

confidence: 99%

Optimal Multi-unit Combinatorial Auctions with Single Minded Bidders

Gujar

Narahari

2009

2009 IEEE Conference on Commerce and Enterprise Computing

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The current art in optimal combinatorial auctions is limited to handling the case of single units of multiple items, with each bidder bidding on exactly one bundle (single minded bidders). This paper extends the current art by proposing an optimal auction for procuring multiple units of multiple items when the bidders are single minded. We develop a procurement auction that minimizes the cost of procurement while satisfying Bayesian incentive compatibility and interim individual rationality. Under appropriate regularity conditions, this optimal auction also satisfies dominant strategy incentive compatibility. The results presented here hold true for equivalent forward auction settings as well.

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“…• Auctions for selling spectrum [40] • Mechanisms for selling advertising space through keyword auctions [41,42,43] • Auctions for selling products/services as part of private marketplaces set up by e-business companies [44] • Bandwidth exchanges [45] • Procurement auctions and private marketplaces for e-procurement [46,47,48] • Logistics and transportation marketplaces [49] • Mechanisms for supply chain formation [50,51,52,53] …”

Section: Mechanism Design and Electronic Commercementioning

confidence: 99%

Foundations of Mechanism Design

Narahari

Narayanam

Garg³

et al. 2009

Game Theoretic Problems in Network Economics and Mechanism Design Solutions

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Mechanism design, an important tool in microeconomics, has recently found widespread applications in modeling and solving decentralized design problems in many branches of engineering, notably computer science, electronic commerce, and network economics. Mechanism design is concerned with settings where a social planner faces the problem of aggregating the announced preferences of multiple agents into a collective decision when the agents exhibit strategic behavior. The objective of this paper is to provide a tutorial introduction to the foundations and key results in mechanism design theory. The paper is in two parts. Part 1 focuses on basic concepts and classical results which form the foundation of mechanism design theory. Part 2 presents key advanced concepts and deeper results in mechanism design.

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Optimal Auctions for Multi-Unit Procurement with Volume Discount Bids

Abstract: Abstract

Cited by 12 publications

References 7 publications

A Mechanism Design Approach to Resource Procurement in Cloud Computing

A Mechanism Design Approach to Resource Procurement in Cloud Computing

Optimal Multi-unit Combinatorial Auctions with Single Minded Bidders

Foundations of Mechanism Design

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