Abstract
Scientific workflows describe a sequence of tasks that together form a scientific experiment. When workflows are computation or data intensive, distributed systems are used. Especially, cloud computing has gained a lot of attention due to its flexible and scalable nature. However, most approaches set up a preconfigured computation clusters or schedule tasks to existing resources. In this paper, we propose the utilization of cloud runtime models and couple them with scientific workflows to create the required architecture of a workflow task at runtime. Hereby, we schedule the architecture state required by a workflow task in order to reduce the overall amount of data transfer and resources needed. Thus, we present an approach that does not schedule tasks to be executed on resources, but schedule architectures to be deployed at runtime for the execution of workflows.
We thank the Simulationswissenschaftliches Zentrum Clausthal-Goettingen (SWZ) for financial support.
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References
Ahmed-Nacer, M., Gaaloul, W., Tata, S.: Occi-compliant cloud configuration simulation. In: 2017 IEEE International Conference on Edge Computing (EDGE), pp. 73–81 (June 2017)
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 ’17, pp. 2:1–2:6. ACM (2017)
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(5), 528–540 (2009)
Deelman, E., et al.: Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci. Program. J. 13(3), 219–237 (2005)
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, vol. 1, pp. 517–525. INSTICC, SciTePress (2018)
Ferry, N., Chauvel, F., Song, H., Rossini, A., Lushpenko, M., Solberg, A.: Cloudmf: Model-driven management of multi-cloud applications. ACM Trans. Internet Technol. 18(2), 16:1–16:24 (2018)
Kacsuk, P., Kovács, J., Farkas, Z.: The flowbster cloud-oriented workflow system to process large scientific data sets. J. Grid Comput. 16(1), 55–83 (2018)
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, vol. 1, pp. 100–111. INSTICC, SciTePress (2018)
Mell, P., Grance, T.: The NIST Definition of Cloud Computing (2011)
Merle, P., Barais, O., Parpaillon, J., Plouzeau, N., Tata, S.: A precise metamodel for open cloud computing interface. In: 2015 IEEE 8th International Conference on Cloud Computing, pp. 852–859 (June 2015)
OASIS: Topology and Orchestration Specification for Cloud Applications (2013). http://docs.oasis-open.org/tosca/TOSCA/v1.0/TOSCA-v1.0.html. Accessed 27 July 2018
OGF: Open Cloud Computing Interface - Core (2016). https://www.ogf.org/documents/GFD.221.pdf. Accessed 27 July 2018
OGF: Open Cloud Computing Interface - Infrastructure (2016). https://www.ogf.org/documents/GFD.224.pdf. Accessed 27 July 2018
OGF: Open Cloud Computing Interface - Platform (2016). https://www.ogf.org/documents/GFD.227.pdf. Accessed 27 July 2018
Qasha, R., Cala, J., Watson, P.: Dynamic deployment of scientific workflows in the cloud using container virtualization. In: 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 269–276 (Dec 2016)
da Silva, R.F., et al.: Toward fine-grained online task characteristics estimation in scientific workflows. In: Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science, WORKS ’13, pp. 58–67. ACM (2013)
Wolstencroft, K., et al.: The taverna workflow suite: designing and executing workflows of web services on the desktop, web or in the cloud. Nucl. Acids Res. 41(W1), W557–W561 (2013)
Zalila, F., Challita, S., Merle, P.: A model-driven tool chain for OCCI. In: Panetto, H. (ed.) OTM 2017. LNCS, vol. 10573, pp. 389–409. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_26
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Erbel, J., Korte, F., Grabowski, J. (2018). Scheduling Architectures for Scientific Workflows in the Cloud. In: Khendek, F., Gotzhein, R. (eds) System Analysis and Modeling. Languages, Methods, and Tools for Systems Engineering. SAM 2018. Lecture Notes in Computer Science(), vol 11150. Springer, Cham. https://doi.org/10.1007/978-3-030-01042-3_2
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DOI: https://doi.org/10.1007/978-3-030-01042-3_2
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