[Back]

T. Mastelic, W. Fdhila, I. Brandic, S. Rinderle-Ma:
"Predicting Resource Allocation and Costs for Business Processes in the Cloud";
Talk: IEEE 11th World Congress on Services (SERVICES 2015), New York, USA; 06-27-2015 - 07-02-2015; in: "2015 IEEE World Congress on Services", (2015), ISBN: 978-1-4673-7275-6; 8 pages.

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.

http://dx.doi.org/10.1109/SERVICES.2015.16