Mots-clés : microservices, Kafka
@article{VYURV_2019_8_4_a6,
author = {A. B. Alaasam and G. I. Radchenko and A. N. Tchernykh},
title = {Micro-workflows: a combination of workflows and data streaming to support digital twins of production processes},
journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a Vy\v{c}islitelʹna\^a matematika i informatika},
pages = {100--116},
year = {2019},
volume = {8},
number = {4},
language = {ru},
url = {http://geodesic.mathdoc.fr/item/VYURV_2019_8_4_a6/}
}
TY - JOUR AU - A. B. Alaasam AU - G. I. Radchenko AU - A. N. Tchernykh TI - Micro-workflows: a combination of workflows and data streaming to support digital twins of production processes JO - Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika PY - 2019 SP - 100 EP - 116 VL - 8 IS - 4 UR - http://geodesic.mathdoc.fr/item/VYURV_2019_8_4_a6/ LA - ru ID - VYURV_2019_8_4_a6 ER -
%0 Journal Article %A A. B. Alaasam %A G. I. Radchenko %A A. N. Tchernykh %T Micro-workflows: a combination of workflows and data streaming to support digital twins of production processes %J Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika %D 2019 %P 100-116 %V 8 %N 4 %U http://geodesic.mathdoc.fr/item/VYURV_2019_8_4_a6/ %G ru %F VYURV_2019_8_4_a6
A. B. Alaasam; G. I. Radchenko; A. N. Tchernykh. Micro-workflows: a combination of workflows and data streaming to support digital twins of production processes. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 8 (2019) no. 4, pp. 100-116. http://geodesic.mathdoc.fr/item/VYURV_2019_8_4_a6/
[1] O. Y. Al-Jarrah, et al., “Efficient Machine Learning for Big Data: A Review”, Big Data Research, 2 (2015), 87–93 | DOI
[2] A. B. Alaasam, et al., “Scientific Micro-Workflows: Where Event-Driven Approach Meets Workflows to Support Digital Twins”, Proceedings of the international conference Russian Supercomputing Days, RuSCDays’18 (Moscow, Russia, September, 24–25, 2018). MSU, 1 (2018), 24–25
[3] I. Altintas, et al., “Kepler: an extensible system for design and execution of scientific workflows”, Proceedings of the 16th International Conference on Scientific and Statistical Database Management, 2004, 1, 423–424 | DOI
[4] C. Boettiger, “An introduction to Docker for reproducible research”, ACM SIGOPS Operating Systems Review, 49 (2015), 71–79 | DOI
[5] K. Borodulin, et al., “Towards Digital Twins Cloud Platform : Microservices and Computational Workflows to Rule a Smart Factory”, The 10th Int. Conf. Util. Cloud Comput., UCC ’17 (Austin, Texas, USA, December, 5–8, 2017), 2017, 209–210 | DOI
[6] M. Bryner, “Smart manufacturing: The next revolution”, Chemical Engineering Progress, 2012, 4–12
[7] O. Carvalho, E. Roloff, P. O. Navaux, “A Distributed Stream Processing based Architecture for IoT Smart Grids Monitoring”, Companion Proceedings of the 10th International Conference on Utility and Cloud Computing (2017), ACM Press, 2017, New York, NY, USA, 2017, 9–14 | DOI
[8] J. Davis, et al., “Smart manufacturing, manufacturing intelligence and demand-dynamic performance”, Computers and Chemical Engineering, 47 (2012), 145–156 | DOI
[9] E. Deelman, et al., “Pegasus, a workflow management system for science automation”, Future Generation Computer Systems, 46 (2015), 17–35 | DOI
[10] T. Fahringer, et al., “ASKALON: a Grid application development and computing environment”, The 6th IEEE/ACM International Workshop on Grid Computing, 2005. IEEE, 2005, 10 | DOI
[11] R. Filgueira, et al., “IoT-Hub: New IoT data-platform for Virtual Research Environments”, 10th International Workshop on Science Gateways, IWSG 2018, 2018, 13–15
[12] R. Filguiera, et al., “Dispel4py: A Python framework for data-intensive scientific computing”, International Journal of High Performance Computing Applications, 31 (2017), 316–334 | DOI
[13] I. T. Foster, D. B. Gannon, Cloud Computing for Science and Engineering, MIT Press, 2017, 392 pp.
[14] E. Glaessgen, D. Stargel, “The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles”, 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference (Reston, Virigina, 2012), American Institute of Aeronautics and Astronautics, 1–14 | DOI
[15] A. Hirales-Carbajal, et al., “A Grid simulation framework to study advance scheduling strategies for complex workflow applications”, 2010 IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum, IPDPSW, 2010. IEEE, 2010. R, 1–8 | DOI
[16] A. Hirales-Carbajal, et al., “Multiple Workflow Scheduling Strategies with User Run Time Estimates on a Grid”, Journal of Grid Computing, 2012, 325–346 | DOI
[17] N. Huber, et al., “Evaluating and Modeling Virtualization Performance Overhead for Cloud Environments”, Proc. 1st Int. Conf. Cloud Comput. Serv. Sci., SciTePress - Science and and Technology Publications, Noordwijkerhout, The Netherlands, 2011, 563–573
[18] P. Korambath, et al., “A smart manufacturing use case: Furnace temperature balancing in steam methane reforming process via kepler workflows”, Procedia Computer Science, 2016, 680–689 | DOI
[19] P. S. Kostenetskiy, A. Y. Safonov, “SUSU Supercomputer Resources”, CEUR Workshop Proceedings, 10th Annu. Int. Sci. Conf. Parallel Comput. Technol., PCT 2016 (Arkhangelsk, Russia, March, 29–31, 2016), v. 1576, 2016, 561–573 | DOI
[20] J. Lee, B. Bagheri, H. A. Kao, “A Cyber-Physical Systems architecture for Industry 4.0- based manufacturing systems”, Manufacturing Letters, 2015, 18–23 | DOI
[21] J. Lewis, M. Fowler, Microservices. 2014 } {\tt http://martinfowler.com/articles/ microservices.html
[22] S. Li, L. Xu, “Da, Zhao S. The internet of things: a survey”, Information Systems Frontiers, 2015, 243–259 | DOI
[23] C. Perera, et al., “Sensing as a service model for smart cities supported by Internet of Things”, Transactions on Emerging Telecommunications Technologies, 2014, 81–93 | DOI
[24] G. Radchenko, A. Alaasam, A. Tchernykh, “Micro-Workflows: Kafka and Kepler Fusion to Support Digital Twins of Industrial Processes”, 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion, 2018. R | DOI
[25] G. Radchenko, E. Hudyakova, “A service-oriented approach of integration of computer-aided engineering systems in distributed computing environments”, UNICORE Summit 2012, Proceedings, 2012. R, 57–66
[26] M. Rahman, et al., “Adaptive workflow scheduling for dynamic grid and cloud computing environment”, Concurrency Computation Practice and Experience, 2013, 1816–1842 | DOI
[27] N. Sinha, K. E. Pujitha, J. S. Alex, “Xively based sensing and monitoring system for IoT”, 2015 International Conference on Computer Communication and Informatics, ICCCI, 2015. IEEE, 2015. R, 1–6 | DOI
[28] J. Soldatos, et al., “OpenIoT: Open Source Internet-of-Things in the Cloud”, Lecture Notes in Computer Science, 2015, 13–25 | DOI
[29] F. Tao, et al., “Digital twin-driven product design, manufacturing and service with big data”, International Journal of Advanced Manufacturing Technology, 2017, 9–12 | DOI
[30] I. Taylor, et al., Workflows for e-Science, Springer, London, 2007, 523 pp.
[31] E. J. Tuegel, et al., “Reengineering Aircraft Structural Life Prediction Using a Digital Twin”, International Journal of Aerospace Engineering, 2011, 1–14 | DOI
[32] M. G. Xavier, et al., “Performance Evaluation of Container-Based Virtualization for High Performance Computing Environments”, 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (Belfast, UK, February, 27– March, 1, 2013), IEEE, Belfast, UK, 2013, 233–240 | DOI