Smart Process Optimization and Adaptive Execution with Semantic Services in Cloud Manufacturing †

A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. In order to address this collaboration challenge, we propose a novel pragmatic approach for the process analysis, implementation and execution. This is achieved through sets of semantic annotations of business process models encoded into BPMN 2.0 extensions. Building blocks for such manufacturing processes are the individual available services, which are also semantically annotated according to the Everything-as-a-Service (XaaS) principles and stored into a common marketplace. The optimization of such manufacturing processes combines pattern-based semantic composition of services with their non-functional aspects. This is achieved by means of Quality-of-Service (QoS)-based Constraint Optimization Problem (COP) solving, resulting in an automatic implementation of service-based manufacturing processes. The produced solution is mapped back to the BPMN 2.0 standard formalism by means of the introduced extension elements, fully detailing the enactable optimal process service plan produced. This approach allows enacting a process instance, using just-in-time service leasing, allocation of resources and dynamic replanning in the case of failures. This proposition provides the best compromise between external visibility, control and flexibility. In this way, it provides an optimal approach for business process models’ implementation, with a full service-oriented taste, by implementing user-defined QoS metrics, just-in-time execution and basic dynamic repairing capabilities. This paper presents the described approach and the technical architecture and depicts one initial industrial application in the manufacturing domain of aluminum forging for bicycle hull body forming, where the advantages stemming from the main capabilities of this approach are sketched.

[1]  Tomasz Kaczmarek,et al.  Semantic Annotation and Composition of Business Processes with Maestro , 2008, ESWC.

[2]  Witold Abramowicz,et al.  Semantically enhanced Business Process Modelling Notation , 2007, SBPM.

[3]  Dieter Fensel,et al.  Semantic business process management: a vision towards using semantic Web services for business process management , 2005, IEEE International Conference on e-Business Engineering (ICEBE'05).

[4]  Manuel Mucientes,et al.  An Integrated Semantic Web Service Discovery and Composition Framework , 2015, IEEE Transactions on Services Computing.

[5]  Aleksander Slominski,et al.  Building a Multi-tenant Cloud Service from Legacy Code with Docker Containers , 2015, 2015 IEEE International Conference on Cloud Engineering.

[6]  Jan Mendling,et al.  Cost-Efficient Scheduling of Elastic Processes in Hybrid Clouds , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[7]  G. Seliger,et al.  Opportunities of Sustainable Manufacturing in Industry 4.0 , 2016 .

[8]  Matthias Klusch Overview of the S3 Contest: Performance Evaluation of Semantic Service Matchmakers , 2012, Semantic Web Services, Advancement through Evaluation.

[9]  Matthias Klusch,et al.  Semantic composition of optimal process service plans in manufacturing with ODERU , 2018, Int. J. Web Inf. Syst..

[10]  Marco Taisch,et al.  ICT in manufacturing: Trends and challenges for 2020 — An European view , 2012, IEEE 10th International Conference on Industrial Informatics.

[11]  Jan Mendling,et al.  Beyond soundness: on the verification of semantic business process models , 2010, Distributed and Parallel Databases.

[12]  David L. Martin,et al.  Semantic Web Services , 2012, Springer Berlin Heidelberg.

[13]  Anja Strunk QoS-Aware Service Composition: A Survey , 2010, 2010 Eighth IEEE European Conference on Web Services.

[14]  Bo Hu,et al.  Everything as a Service (XaaS) on the Cloud: Origins, Current and Future Trends , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[15]  Chao-Chun Chen,et al.  A novel cloud manufacturing framework with auto-scaling capability for the machining industry , 2016, Int. J. Comput. Integr. Manuf..

[16]  Srikumar Venugopal,et al.  Introducing the Vienna Platform for Elastic Processes , 2012, ICSOC Workshops.

[17]  XiangYang,et al.  QoS-Aware Dynamic Composition of Web Services Using Numerical Temporal Planning , 2014 .

[18]  Srikumar Venugopal,et al.  Elastic Business Process Management: State of the art and open challenges for BPM in the cloud , 2014, Future Gener. Comput. Syst..

[19]  M. Brian Blake,et al.  Proactive virtualized resource management for service workflows in the cloud , 2014, Computing.

[20]  Thomi Pilioura,et al.  Unified publication and discovery of semantic Web services , 2009, TWEB.

[21]  Matthias Klusch,et al.  Fast Composition Planning of OWL-S Services and Application , 2006, 2006 European Conference on Web Services (ECOWS'06).

[22]  Mathias Schmitt,et al.  Towards Industry 4.0 - Standardization as the crucial challenge for highly modular, multi-vendor production systems , 2015 .

[23]  Matthias Klusch,et al.  FCE4BPMN: On-demand QoS-based optimised process model execution in the cloud , 2017, 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[24]  F. Iovane,et al.  The Manufuture road: towards competitive and stainable high-adding-value manufacturing , 2009 .

[25]  Sebastian Stein,et al.  A BPMO Based Semantic Business Process Modelling Environment , 2007, SBPM.

[26]  Ying Zhang,et al.  Bring QoS to P2P-based semantic service discovery for the Universal Network , 2009, Personal and Ubiquitous Computing.

[27]  Mathias Weske Business Process Management Architectures , 2012 .

[28]  Mazin S. Yousif,et al.  Microservices , 2016, IEEE Cloud Comput..

[29]  Jatinder N. D. Gupta,et al.  Critical Path-Based Iterative Heuristic for Workflow Scheduling in Utility and Cloud Computing , 2013, ICSOC.

[30]  Ramakrishnan Rajamony,et al.  An updated performance comparison of virtual machines and Linux containers , 2015, 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[31]  Alberto Bayo-Moriones,et al.  Perceived performance effects of ICT in manufacturing SMEs , 2013, Ind. Manag. Data Syst..

[32]  Jerry R. Hobbs,et al.  DAML-S: Semantic Markup for Web Services , 2001, SWWS.

[33]  Matthias Klusch,et al.  CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance , 2016, KEOD.

[34]  David Bernstein,et al.  Containers and Cloud: From LXC to Docker to Kubernetes , 2014, IEEE Cloud Computing.

[35]  Claude Godart,et al.  Bi-criteria strategies for business processes scheduling in cloud environments with fairness metrics , 2013, IEEE 7th International Conference on Research Challenges in Information Science (RCIS).

[36]  Yixin Chen,et al.  QoS-Aware Dynamic Composition of Web Services Using Numerical Temporal Planning , 2014, IEEE Transactions on Services Computing.

[37]  Bernd Freisleben,et al.  Multi-objective Scheduling of BPEL Workflows in Geographically Distributed Clouds , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[38]  Luca Mazzola,et al.  DLP: a web-based facility for exploration and basic modification of ontologies by domain experts , 2017, iiWAS.

[39]  Matthias Klusch,et al.  The iSeM matchmaker: A flexible approach for adaptive hybrid semantic service selection , 2012, J. Web Semant..

[40]  Matthias Klusch,et al.  ODERU: Optimisation of Semantic Service-Based Processes in Manufacturing , 2017, KESW.

[41]  Schahram Dustdar,et al.  Optimization of Complex Elastic Processes , 2016, IEEE Transactions on Services Computing.

[42]  Matthias Klusch,et al.  Semantic Web Service Search: A Brief Survey , 2016, KI - Künstliche Intelligenz.