Scientific progress of design research artefacts

Many existing IT applications exhibit strongly varying demand patterns for resources. Accommodating an ever increasing and highly fluctuating demand requires continuous availability of sufficient resources. To achieve this state at reasonably costs, a high degree of flexibility with respect to the given IT infrastructure is necessary. Facing this challenge the idea of Cloud computing has been gaining interest. In so-called Clouds resources such as CPU, storage and bandwidth can be bundled into a single services, which are offered to Cloud users. These services can be accessed in oblivion of the underlying IT infrastructure. This way Cloud Computing facilitates the introduction of new products and services without large investments in the IT infrastructure. Cloud Computing is a promising approach with a high impact on business models. One aspect of business models is clearly the revenue model, which defines how prices should be set to achieve predefined revenue level. The decision about accepting or denying requests has a high impact on the revenue of the provider. In this paper we analyze two approaches that support the cloud provider in its decision. We show that predefined policies allow increasing revenue compared to widely used technical models such as first-come-first-serve

A Heuristic Approach for Capacity Control in Clouds

Cloud resource providers in a market face dynamic and unpredictable consumer behavior. The way, how prices are set in a dynamic environment, can influence the demand behavior of price sensitive customers. A Cloud resource provider has to decide on how to allocate his scarce resources in order to maximize his profit. The application of bid price control for evaluating incoming service requests is a common approach for capacity control in network revenue management. In this paper we introduce a customized version of the concept of selfadjusting bid prices and apply it to the area of Cloud Computing. Furthermore, we perform a simulation in order to test the efficiency of the proposed model

Making money with clouds: Revenue optimization through automated policy decisions

Many existing IT applications exhibit strongly varying demand patterns for resources. Accommodating an ever increasing and highly fluctuating demand requires continuous availability of sufficient resources. To achieve this state at reasonably costs, a high degree of flexibility with respect to the given IT infrastructure is necessary. Facing this challenge the idea of Cloud computing has been gaining interest. In so-called Clouds resources such as CPU, storage and bandwidth can be bundled into a single services, which are offered to Cloud users. These services can be accessed in oblivion of the underlying IT infrastructure. This way Cloud Computing facilitates the introduction of new products and services without large investments in the IT infrastructure. Cloud Computing is a promising approach with a high impact on business models. One aspect of business models is clearly the revenue model, which defines how prices should be set to achieve predefined revenue level. The decision about accepting or denying requests has a high impact on the revenue of the provider. In this paper we analyze two approaches that support the cloud provider in its decision. We show that predefined policies allow increasing revenue compared to widely used technical models such as first-come-first-serve