Elastic Processing Support for the Manufacturing Industry



In volatile domains, like the manufacturing industry, business process landscapes may be made up from thousands of different process definitions and instances. As a result, a Business Process Management System (BPMS) needs to be able to handle the concurrent execution of a very large number of processes. Many of the processes may be resource-intensive, leading to ever-changing requirements regarding the needed computing resources to execute them. Using Cloud technologies, it is possible to allocate resources for distinct process steps, which are obtained on demand from Cloud platform providers, taking into account resource, quality, and cost elasticity.

One of the most important requirements for a efficient resource provisioning strategy is the prediction of future resource requirements, while respecting the concept of Elastic Processes. Current approaches only focus on the ad hoc allocation of Cloud resources, which allows them to only estimate future resource requirements based on the current requirements. If taking into account the process perspective, it is possible to actually calculate future computing resources, as future steps are known in advance. This more reliable forecasting approach enables the BPMS to perform a more efficient resource allocation and to avoid over- respectively under-provisioning.

During the course of our research, we realized, that these resource allocation challenges can also be found in the upcoming integration of the manufacturing machines, based on the advances of the Internet of Things. The unbound data streams among the different machines also exhibit ever-changing resource requirements which motivated us to evaluate to also evaluate the feasibility of our algorithms for the data stream processing domain.

Project goals

The overall goal of this project is to develop and implement a resource allocation and process scheduling algorithm, which combines the IT infrastructure and the BPM perspective. The work can be divided into three work packages:

  • Predict the resource usage of single process steps based continuous feedback from the BPMS.
  • Implementation of an Elastic Reasoning Algorithm, which takes into account resource, cost and quality elasticity to reason about optimal resource allocation and process scheduling.
  • Develop a Elastic Reasoning Heuristic to reduce the reasoning time for the NP-hard multi-objective optimization problem.


This project is funded by the Technical University of Vienna as an Innovative Project.


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