Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined.By analyzing the spot price histories of Amazon's EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. We find that prices are usually not market-driven as sometimes previously assumed. Rather, they are typically generated at random from within a tight price interval via a dynamic hidden reserve price. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.

Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined. By analyzing the spot price histories of Amazon’s EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. Our findings suggest that usually prices are not market-driven, as sometimes previously assumed. Rather, they are likely to be generated most of the time at random from within a tight price range via a dynamic hidden reserve price mechanism. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.

Abstract-Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the selection of environments that can both fulfill deadlines and fit budgets. To address this situation, we introduce the ExPERT BoT scheduling framework. Our framework systematically selects from a large search space the Pareto-efficient scheduling strategies, that is, the strategies that deliver the best results for both makespan and cost. ExPERT chooses from them the best strategy according to a general, user-specified utility function. Through simulations and experiments in real production environments, we demonstrate that ExPERT can substantially reduce both makespan and cost in comparison to common scheduling strategies. For bioinformatics BoTs executed in a real mixed grid+cloud environment, we show how the scheduling strategy selected by ExPERT reduces both makespan and cost by 30%-70%, in comparison to commonlyused scheduling strategies.

CLOUD COMPUTING IS taking the computer world by storm. Infrastructure-as-a-Service (IaaS) clouds (such as Amazon Elastic Compute Cloud, and EC2) allow anyone with a credit card to tap into a seemingly unlimited fountain of computing resources by renting virtual machines for several cents or dollars per hour. Forrester Research 30 predicted the cloud computing market could top $241 billion in 2020, compared to $40.7 billion in 2010, a sixfold increase. What will the 2020 clouds look like? Given the pace of innovation in cloud computing and other utilities (such as smart grids and wireless spectra), substantial shifts are bound to occur in the way providers design, operate, and sell cloud computing resources and how clients purchase and use them. key insights Current trends toward increased flexibility and efficiency in IaaS clouds will force a business model paradigm shift on the cloud storage industry. The RaaS cloud is the likely result of this shift. The RaaS cloud uses economic mechanisms within physical machines.

No abstract