Spawn: A Distributed Computational Economy
IEEE Transactions on Software Engineering
Topographically discounted Internet infrastructure resources: a panel study and econometric analysis
Information Technology and Management
Risk Management of Contract Portfolios in IT Services: The Profit-at-Risk Approach
Journal of Management Information Systems
A Market Design for Grid Computing
INFORMS Journal on Computing
Information Systems Research
When Is Price Discrimination Profitable?
Management Science
Risk hedging in storage grid markets: Do options add value to forwards?
ACM Transactions on Management Information Systems (TMIS)
Understanding demand volatility in large VoD systems
Proceedings of the 21st international workshop on Network and operating systems support for digital audio and video
A Clock-and-Offer Auction Market for Grid Resources When Bidders Face Stochastic Computational Needs
INFORMS Journal on Computing
Risk Management and Optimal Pricing in Online Storage Grids
Information Systems Research
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Commodities such as cloud resources storage, computing, bandwidth are often sold to clients on a pay-as-you-go basis. Thus, resource providers absorb all risk arising from end users' demand volatilities. We focus on the revenue risk management of commodities with highly volatile demand profiles using cloud computing as the application domain and bandwidth as the exemplar commodity. We extend the state of the art in risk hedging by introducing a new concept of dynamic forward contracts where a provider and a client flexibly interact through offers and responses over a set of time periods in a horizon. We develop an optimal pricing mechanism that takes into account the risk propensities of the provider and the client. The overall mechanism is modeled as a pair of nested dynamic programs denoting the offer-response interactions. The mechanism also incorporates two learning components: short-term learning on the client's demand and long-term learning on the client's risk propensity. We characterize two approaches for predicting the client's demand---a recursive demand prediction model and an aggregate demand prediction model. Detailed experimental studies of the proposed mechanism using real Web traffic data on the clients of Amazon Web Services have been carried out. The empirical results clearly demonstrate the superiority of the proposed mechanism over benchmark mechanisms such as the current industry practice of spot markets and static forward pricing mechanisms proposed in the literature in ex ante and ex post settings. The results also highlight key interaction effects among parameters controllable by a provider and the risk propensities of the market players, leading to valuable managerial implications for the practical adoption of the proposed mechanism.