Performability-oriented description and analysis of business processes

Business Process Management (BPM) is an holistic approach for describing, analyzing, executing, managing and improving large enterprise business processes, which can be seen as collections of related tasks executed to accomplish well-defined goals. This chapter focuses on the description and analysis of business processes. In particular, the chapter introduces a notation for the description of a business process in terms of both functional and non-functional properties. Such a description is then used to carry out the predictive analysis of the business process behaviour. The chapter specifically addresses the performance and reliability prediction of a business process by use of a joint measure known as performability. In the BPM context, the Business Process Modeling Notation (BPMN) is the de-facto standard for the high-level description of business processes. Unfortunately BPMN does not support the characterization of the business process in terms of non-functional properties such as performance and reliability. To overcome such limitation, this chapter introduces PyBPMN (Performability-enabled BPMN), a lightweight BPMN extension for the specification of performability properties. The proposed extension is based on an approach that exploits principles and standards introduced by MDA (Model Driven Architecture). In particular, the BPMN extension is carried out by first specifying the BPMN metamodel and then obtaining the PyBPMN metamodel by adding the metaclasses that define the specific performance and reliability characteristics. The tasks that define a business process can be carried out either by human operators or by automated software services. This chapter specifically focuses on fully automated business processes that are defined and executed as orchestrations of software services. In this respect, PyBPMN can be used to describe the performance and reliability properties of both a business process and its constituent services. This chapter also introduces a model-driven method that makes use of PyBPMN to predict, at design time, the performability of a business process, either to select the configuration of services that provide the best level of performability or to check if a given configuration satisfies the overall requirements of the business process. The proposed method can be fully automated, thus allowing business analysts to carry out the performability prediction with no extra effort and without being required to own specific skills of performability theory, as shown by use of an example case study. © 2011 by Nova Science Publishers, Inc. All rights reserved.