Abstract
Executing dynamic simulations in a distributed environment allows saving resources and time which is a desired goal in research and industry. One example dynamic simulation is the multi-level-simulation. Here, specific parts of the simulation can be inspected on different levels of detail at runtime. To cope with the changing simulation requirements an elastic and scalable infrastructure is required, as well as an approach adjusting the infrastructure to the simulation needs. In this paper, we enhance a former approach coupling workflows with architectural needs to utilize monitored runtime information and support decision making. Moreover, we demonstrate the concept of executing dynamic simulations over a workflow based approach by dynamically choosing the levels of detail within a supply chain multi-level-simulation.
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The implementation of the presented approach, as well as videos demonstrating the example scenario, can be found at: https://gitlab.gwdg.de/rwm/.
References
Armbrust, M., et al.: Above the Clouds: A Berkeley View of Cloud Computing. Electrical Engineering and Computer Sciences, University of California at Berkeley (2009)
Beni, E.H., Lagaisse, B., Joosen, W.: Adaptive and reflective middleware for the cloudification of simulation & optimization workflows. In: Proceedings of the 16th Workshop on Adaptive and Reflective Middleware (ARM) (2017)
Brun, Y., et al.: Engineering self-adaptive systems through feedback loops. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 48–70. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_3
Deelman, E., Gannon, D., Shields, M., Taylor, I.: Workflows and e-science: an overview of workflow system features and capabilities. Futur. Gener. Comput. Syst. 25, 528–540 (2009)
Deelman, E., et al.: Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci. Program. J. 13, 219–237 (2005)
Erbel, J., Brand, T., Giese, H., Grabowski, J.: OCCI-compliant, fully causal-connected architecture runtime models supporting sensor management. In: Proceedings of the 14th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (2019)
Erbel, J., Korte, F., Grabowski, J.: Comparison and runtime adaptation of cloud application topologies based on OCCI. In: Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER) (2018)
Erbel, J., Korte, F., Grabowski, J.: Scheduling architectures for scientific workflows in the cloud. In: Khendek, F., Gotzhein, R. (eds.) SAM 2018. LNCS, vol. 11150, pp. 20–28. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01042-3_2
IBM: An architectural blueprint for autonomic computing. IBM White Paper (2005)
Kacsuk, P., Kovács, J., Farkas, Z.: The flowbster cloud-oriented workflow system to process large scientific data sets. J. Grid Comput. 16, 55–83 (2018). https://doi.org/10.1007/s10723-017-9420-4
Kleppe, A.G., Warmer, J.B., Bast, W.: MDA Explained: The Model Driven Architecture: Practice and Promise. Addison-Wesley Professional, Boston (2003)
Korte, F., Challita, S., Zalila, F., Merle, P., Grabowski, J.: Model-driven configuration management of cloud applications with OCCI. In: Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER) (2018)
Kühne, T.: Matters of (meta-) modeling. Softw. Syst. Model. 5, 369–385 (2006). https://doi.org/10.1007/s10270-006-0017-9
Mell, P., Grance, T.: The NIST Definition of Cloud Computing. National Institute of Standards and Technology (2009)
Mens, T., Van Gorp, P.: A taxonomy of model transformation. Electron. Notesin Theor. Comput. Sci. 152, 125–142 (2006)
Merle, P., Barais, O., Parpaillon, J., Plouzeau, N., Tata, S.: A precise metamodel for open cloud computing interface. In: Proceedings of 8th IEEE International Conference on Cloud Computing (CLOUD) (2015)
Object Management Group: OMG: Business Process Model and Notation (2011). http://www.omg.org/spec/BPMN/2.0/PDF. Accessed 29 July 2019
Object Management Group: Unified Modeling Language (2015). http://www.omg.org/spec/UML/2.5/PDF. Accessed 29 July 2019
Open Grid Forum: Open Cloud Computing Interface - Core (2016). https://www.ogf.org/documents/GFD.221.pdf. Accessed 29 July 2019
Open Grid Forum: Open Cloud Computing Interface - Infrastructure (2016). https://www.ogf.org/documents/GFD.224.pdf. Accessed 29 July 2019
Open Grid Forum: Open Cloud Computing Interface - Platform (2016). https://www.ogf.org/documents/GFD.227.pdf. Accessed 29 July 2019
Open Grid Forum: Open Cloud Computing Interface - Service Level Agreements (2016). https://www.ogf.org/documents/GFD.228.pdf. Accessed 29 July 2019
OpenStack: Newton (2016). https://releases.openstack.org/newton/. Accessed 29 July 2019
Qasha, R., Cala, J., Watson, P.: Dynamic deployment of scientific workflows in the cloud using container virtualization. In: Proceedings of the 8th IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (2016)
Wittek, S., Rausch, A.: Learning state mappings in multi-level-simulation. In: Baum, M., Brenner, G., Grabowski, J., Hanschke, T., Hartmann, S., Schöbel, A. (eds.) SimScience 2017. CCIS, vol. 889, pp. 208–218. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96271-9_13
Wolstencroft, K., et al.: The Taverna workflow suite: designing and executing workflows of web services on the desktop, web or in the cloud. Nucleic Acids Res. 41, W557–W561 (2013)
Zalila, F., Challita, S., Merle, P.: A model-driven tool chain for OCCI. In: Proceedings of the 25th International Conference on Cooperative Information Systems (CoopIS) (2017)
Acknowledgment
We thank the Simulationswissenschaftliches Zentrum Clausthal-Goettingen for financial support.
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Erbel, J., Wittek, S., Grabowski, J., Rausch, A. (2020). Dynamic Management of Multi-level-simulation Workflows in the Cloud. In: Gunkelmann, N., Baum, M. (eds) Simulation Science. SimScience 2019. Communications in Computer and Information Science, vol 1199. Springer, Cham. https://doi.org/10.1007/978-3-030-45718-1_2
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