Predicting Resource Allocation and Costs for Business Processes in the Cloud

Toni Mastelic
Walid Fdhila
Ivona Brandic
Stefanie Rinderle-Ma
Speech with CD or web proceedings
2015 IEEE World Congress on Services
ISBN: 978-1-4673-7275-6
By moving business processes into the cloud,<br> business partners can benefit from lower costs, more flexibility<br> and greater scalability in terms of resources offered by the<br> cloud providers. In order to execute a process or a part of it, a<br> business process owner selects and leases feasible resources<br> while considering different constraints; e.g., optimizing resource<br> requirements and minimizing their costs. In this context,<br> utilizing information about the process models or the dependencies<br> between tasks can help the owner to better manage<br> leased resources. In this paper, we propose a novel resource<br> allocation technique based on the execution path of the process,<br> used to assist the business process owner in efficiently leasing<br> computing resources. The technique comprises three phases,<br> namely process execution prediction, resource allocation and<br> cost estimation. The first exploits the business process model<br> metrics and attributes in order to predict the process execution<br> and the required resources, while the second utilizes this<br> prediction for efficient allocation of the cloud resources. The<br> final phase estimates and optimizes costs of leased resources by<br> combining different pricing models offered by the provider.
TU Focus: 
Information and Communication Technology

T. Mastelic, W. Fdhila, I. Brandic, S. Rinderle-Ma:
"Predicting Resource Allocation and Costs for Business Processes in the Cloud";
Vortrag: IEEE 11th World Congress on Services (SERVICES 2015), New York, USA; 27.06.2015 - 02.07.2015; in: "2015 IEEE World Congress on Services", (2015), ISBN: 978-1-4673-7275-6; 8 S.

Zusätzliche Informationen

Last changed: 
07.12.2015 14:52:30
TU Id: 
Department Focus: 
Business Informatics
Abstract German: 
Author List: 
T. Mastelic, W. Fdhila, I. Brandic, S. Rinderle-Ma