2014 IEEE International Conference on Cloud Engineering (IC2E) (2014)
Boston, MA, USA
March 11, 2014 to March 14, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IC2E.2014.13
Business processes orchestrate service requests in a structured fashion. Process knowledge, however, has rarely been used to predict and decide about cloud infrastructure resource usage. In this paper, we present an approach for BPM-aware cloud computing that builds on process knowledge to improve the timeliness and quality of resource scaling decisions. We introduce an IaaS resource controller based on fuzzy theory that monitors process execution and that is used to predict and control resource requirements for subsequent process tasks. In a laboratory experiment, we evaluate the controller design against a commercially available state-of-the-art auto scaler. Based on the results, we discuss improvements and limitations, and suggest directions for further research.
Process control, Business, Pragmatics, Complexity theory, Computational modeling, Cloud computing, Virtual machining
S. Euting, C. Janiesch, R. Fischer, S. Tai and I. Weber, "Scalable Business Process Execution in the Cloud," 2014 IEEE International Conference on Cloud Engineering (IC2E), Boston, MA, USA, 2014, pp. .