Efficient Contracting in Cloud Service Markets with Asymmetric Information - A Screening Approach

Increasing popularity of cloud-based services has led to the emergence of cloud marketplaces where services from different providers are offered, usually in the form of a catalog. The customers' decision about buying offered services is based on idiosyncratic preferences regarding non-functional service attributes, e.g., price, provider reputation, and quality of service. The customers' preferences are not necessarily known to providers at the time the service (including pricing) is defined in the marketplace's service catalog. Thus, from a microeconomic perspective, we have to deal with information asymmetry on incomplete markets. On such markets, finding the optimal contracts (i.e., non-functional characteristics and prices) that maximize the provider's profit is challenging due to information uncertainty. This paper presents a generic economic framework based on contract theory which solves the above-mentioned optimization for cloud-based services offered at a marketplace. The contribution is threefold: (i) we analyze and select from providers' perspective non-functional attributes considered by customers when deciding which services to buy, (ii) we implement a holistic contracting framework that grants providers maximal profit through optimal combination of potential values of the chosen attributes and (iii) we present a study of a desktop service use case. The contracting framework addresses the phenomenon of adverse selection by leveraging the screening technique.