Efficient Risk Hedging by Dynamic Forward Pricing: A Study in Cloud Computing

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 dearly 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.