Predicting Resource Allocation and Costs for Business Processes in the Cloud

By moving business processes into the cloud, business partners can benefit from lower costs, more flexibility and greater scalability in terms of resources offered by the cloud providers. In order to execute a process or a part of it, a business process owner selects and leases feasible resources while considering different constraints; e.g., optimizing resource requirements and minimizing their costs. In this context, utilizing information about the process models or the dependencies between tasks can help the owner to better manage leased resources. In this paper, we propose a novel resource allocation technique based on the execution path of the process, used to assist the business process owner in efficiently leasing computing resources. The technique comprises three phases, namely process execution prediction, resource allocation and cost estimation. The first exploits the business process model metrics and attributes in order to predict the process execution and the required resources, while the second utilizes this prediction for efficient allocation of the cloud resources. The final phase estimates and optimizes costs of leased resources by combining different pricing models offered by the provider.