With the rapidly growing demand for the cloud services, a need for efficient methods to trade computing resources increases. Commonly used fixed-price model is not always the best approach for trading cloud resources, because of its inflexible and static nature. Dynamic trading systems, which make use of market mechanisms, show promise for more efficient resource allocation and pricing in the cloud. However, most of the existing mechanisms ignore the seller's costs of providing the resources. In order to address it, we propose a single-sided market mechanism for trading virtual machine instances in the cloud, where the cloud provider can express the reservation prices for traded cloud services. We investigate the theoretical properties of the proposed mechanism and prove that it is truthful, i.e. the buyers do not have an incentive to lie about their true valuation of the resources. We perform extensive experiments in order to investigate the impact of the reserve price on the market outcome. Our experiments show that the proposed mechanism yields near optimal allocations and has a low execution time.
@cite_6 consider a periodical combinatorial auction with a single seller. They propose to determine the resource prices based on limited English combinatorial auction model, and to optimally allocate resources in different timeframes based on genetic algorithm. Their proposed mechanism does not guarantee truthfulness. @cite_4 describe a marketplace for trading cloud resources based on e-Bay-like transaction model that supports different services with different level of job priorities. Their marketplace contains an auction system that encourages truthful bidding. Unlike their work, we consider combinatorial market setting with a unique seller without the requirement to prioritize the requests.
@cite_4 @cite_6
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On account of the resource characteristics under cloud computing environment and the flexibility and availability of applying economic mechanism to resource allocation, a resource allocation model based on the limited English combinatorial auction under cloud computing environment is advanced. An improved periodical auction model is constructed, and then ribbon capacity is adopted to describe the special storage capacity for special users. Based on the above, the resource trading price between resource buyers and resource providers is determined based on the limited English combinatorial auction model. In the end, the optimal resource allocation solution is pursued based on genetic algorithm. Simulation results have shown that the proposed model is both feasible and effective, which can maximize the seller total trading amounts as well as reduce the executing time of winner determination. In this paper, we present a novel combinatorial auction (CA) based trading infrastructure to enable the supply and demand of Cloud services from different Cloud providers (CP). We propose a new auction policy that considers the relationship among CPs (mutual business relationship) in bidding mechanism. In our new auction-based market model, we allow the group of service providers to publish their bids collaboratively as a single bid to the auctioneer. It gives service providers a chance to autonomously find partners and make groups. Then they can use group strategy to increase their competitive power and compete for winning the bid (s). This will reduce conflicts, as well as collaboration cost and negotiation time, among participants as compare to existing CA-based market model. We implement our proposed market model of trading service in a simulated environment and study its economic efficiency with existing model. We study anonymous posted price mechanisms for combinatorial auctions in a Bayesian framework. In a posted price mechanism, item prices are posted, then the consumers approach the seller sequentially in an arbitrary order, each purchasing her favorite bundle from among the unsold items at the posted prices. These mechanisms are simple, transparent and trivially dominant strategy incentive compatible (DSIC). We show that when agent preferences are fractionally subadditive (which includes all submodular functions), there always exist prices that, in expectation, obtain at least half of the optimal welfare. Our result is constructive: given black-box access to a combinatorial auction algorithm A, sample access to the prior distribution, and appropriate query access to the sampled valuations, one can compute, in polytime, prices that guarantee at least half of the expected welfare of A. As a corollary, we obtain the first polytime (in n and m) constant-factor DSIC mechanism for Bayesian submodular combinatorial auctions, given access to demand query oracles. Our results also extend to valuations with complements, where the approximation factor degrades linearly with the level of complementarity. We study anonymous posted price mechanisms for combinatorial auctions in a Bayesian framework. In a posted price mechanism, item prices are posted, then the consumers approach the seller sequentially in an arbitrary order, each purchasing her favorite bundle from among the unsold items at the posted prices. These mechanisms are simple, transparent and trivially dominant strategy incentive compatible (DSIC). We show that when agent preferences are fractionally subadditive (which includes all submodular functions), there always exist prices that, in expectation, obtain at least half of the optimal welfare. Our result is constructive: given black-box access to a combinatorial auction algorithm A, sample access to the prior distribution, and appropriate query access to the sampled valuations, one can compute, in polytime, prices that guarantee at least half of the expected welfare of A. As a corollary, we obtain the first polytime (in n and m) constant-factor DSIC mechanism for Bayesian submodular combinatorial auctions, given access to demand query oracles. Our results also extend to valuations with complements, where the approximation factor degrades linearly with the level of complementarity.