Elastic Processing Support for the Manufacturing Industry

 

Motivation

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.

Acknowledgements

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

Publications

  • M. Borkowski, C. Hochreiner, and S. Schulte (2017). Moderated Resource Elasticity for Stream Processing Applications. In International Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017) at 23rd International European Conference on Parallel and Distributed Computing (Euro-Par 2017), pages NN-NN. Santiago de Compostela, Spain.
  • C. Hochreiner, M. Vögler, J. M. Schleicher, C. Inzinger, S. Schulte and S. Dustdar (2017). Nomadic Applications Traveling in the Fog. In 2nd EAI International Conference on Cloud, Networking for IoT Systems (CN4IoT), pages NN-NN. Brindisi, Italy.
  • L. Mazzola, P. Kapahnke, P. Waibel, C. Hochreiner, and M. Klusch (2017). FCE4BPMN: On-demand QoS-based Optimised Process Model Execution in the Cloud. In 2017 IEEE International Conference on Engineering, Technology and Innovation/ International Technology Management Conference (ICE/ITMC), pages 319-328. IEEE Computer Society, Washington, DC, USA.
  • M. Nardelli, C. Hochreiner and S. Schulte (2017). Elastic Provisioning of Virtual Machines for Container Deployment. In International Workshop on Autonomous Control for Performance and Reliability Trade-Offs in Internet of Services (ACPROSS 2017) at 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017), pages 5-10. L'Aquila, Italy.
  • C. Hochreiner (2017). VISP Testbed - A Toolkit for Modeling and Evaluating Resource Provisioning Algorithms for Stream Processing Applications. In 9th ZEUS Workshop, volume 1826 CEUR-WS, pages 37-44, Lugano, Switzerland.
  • P. Hoenisch, D. Schuller, S. Schulte, C. Hochreiner, S. Dustdar (2016). Optimization of Complex Elastic Processes. In IEEE Transactions on Services Computing, Volume 9, Number, 5, 700-713.
  • P. Waibel, C. Hochreiner, S.Schulte (2016). Cost-Efficient Data Redundancy in the Cloud. In 9th IEEE International Conference on Service Oriented Computing and Applications (SOCA 2016), pages 1-9. IEEE Computer Society, Washington, DC, USA.
  • M. Borkowski, S.Schulte, C. Hochreiner (2016). Predicting Cloud Resource Utilization. In 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2016), pages 37-42. IEEE/ACM Shanghai, China.
  • C. Hochreiner, P. Waibel, M. Borkowski (2016). Bridging Gaps in Cloud Manufacturing with 3D Printing. In Proceedings of Informatik 2016, volume 259 of Lecture Notes in Informatics, pages 1623-1626 Gesellschaft für Informatik, Bonn.
  • C. Hochreiner, M. Vögler, P. Waibel, S. Dustdar (2016). VISP: An Ecosystem for Elastic Data Stream Processing for the Internet of Things. In 20th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2016), pages 19-29. IEEE Computer Society, Washington, DC, USA.
  • C. Hochreiner, M. Vögler, S. Schulte, S. Dustdar (2016). Elastic Stream Processing for the Internet of Things. In IEEE 9th International Conference on Cloud Computing (CLOUD 2016), pages 100-107. IEEE Computer Society, Washington, DC, USA.
  • C. Hochreiner, S. Schulte, S. Dustdar, F. Lecue (2015). Elastic Stream Processing for Distributed Environments. In IEEE Internet Computing, Volume 19, Number 6, pages 54-59.
  • P. Hoenisch, C. Hochreiner, D. Schuller, S. Schulte, J. Mendling, S. Dustdar (2015). Cost-Efficient Scheduling of Elastic Processes in Hybrid Clouds. In IEEE 8th International Conference on Cloud Computing (CLOUD 2015), pages 17-24. IEEE Computer Society, Washington, DC, USA.
  • C. Hochreiner (2015). Privacy-Aware Scheduling for Inter-Organizational Processes. In 7th Central-European Workshop on Services and their Composition (ZEUS 2015), volume 1360 CEUR-WS, pages 63-68, Jena, Germany.
  • S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume 46, 36-50.
  • S. Schulte, P. Hoenisch, C. Hochreiner, S. Dustdar, M. Klusch, D. Schuller (2014). Towards Process Support for Cloud Manufacturing. In 18th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2014), pages 142-149. IEEE Computer Society, Washington, DC, USA.

People

Contact

s.schulte@infosys.tuwien.ac.at

Staff