Abstract
In recent years the concept of “containerization” thanks to commercial software development has become a very promising paradigm also for e-science. In this paper, we present how this paradigm may significantly facilitate the creation of platforms for advanced scientific simulations. We present how with currently available containerization technologies, you can design a platform for simulation in the field of computational medicine with the same functionality as cloud system called Atmosphere, developed in 2011-14 ACK Cyfronet AGH for the Virtual Physiological Human community. The Atmosphere, based on the virtualization concept, provides support thanks to an intuitive interface that performs workflows on-demand cloud computing as well as access to cloud storage. After careful analysis of the Atmosphere structure, a redesign of the platform was subsequently carried out using container-based technologies, in particular Kubernetes. After architectural remodeling based on analysis of requirements and containerization possibilities, an implementation has followed through a Maven project in Java using the Kubernetes API. All this was followed by a validation phase to verify the actual operation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aversa, R., Branco, D., Di Martino, B., Venticinque, S.: Container based simulation of electric vehicles charge optimization. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 227, pp. 117–126. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75078-7_13
Di Martino, B., Cretella, G., Esposito, A.: Cloud portability and interoperability. In: Cloud Portability and Interoperability. SCS, pp. 1–14. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13701-8_1
Pop, F., Kołodziej, J., Di Martino, B. (eds.): Resource Management for Big Data Platforms. CCN, Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44881-7
Aarestrup, F.M., et al.: Towards a European health research and innovation cloud (HRIC). Genome Med. 12(1), 1–14 (2020)
Martí-Bonmatí, L., et al.: Primage project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. Eur. Radiol. Exp. 4(1), 1–11 (2020)
Kasztelnik, M., et al.: Support for taverna workflows in the VPH-share cloud platform. Comput. Methods Prog. Biomed. 146, 37–46 (2017)
Meizner, J., Nowakowski, P., Kapala, J., Wojtowicz, P., Bubak, M., Tran, V., et al.: Towards exascale computing architecture and its prototype: services and infrastructure. Comput. Inform. 39(4), 860–880 (2020)
Nowakowski, P., et al.: Cloud computing infrastructure for the VPH community. J. Comput. Sci. 24, 169–179 (2018)
Bubak, M., et al.: The EurValve model execution environment. Interface Focus 11(1), 20200006 (2021)
Malawski, M., Gajek, A., Zima, A., Balis, B., Figiela, K.: Serverless execution of scientific workflows: experiments with HyperFlow, AWS Lambda and Google cloud functions. Futur. Gener. Comput. Syst. 110, 502–514 (2020)
Gerhards, M., Sander, V., Živković, M., Belloum, A., Bubak, M.: New approach to allocation planning of many-task workflows on clouds. Concurr. Comput. Pract. Experience 32(2), e5404 (2020)
Yadav, A.K., Garg, M.L., Ritika: Docker containers versus virtual machine-based virtualization. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol. 814, pp. 141–150. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1501-5_12
Kubernetes documentation. https://kubernetes.io/
Maven documentation. https://maven.apache.org/
Github repository. https://github.com/gennarojuniorpezzullo1/Atmosphere_Container_ Implementation.git
Bellini, E., Cimato, S., Damiani, E., Di Martino, B., Esposito, A.: Towards a trustworthy semantic-aware marketplace for interoperable cloud services. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 606–615. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_61
Di Martino, B., Gracco, S.A.: Semantic techniques for IoT sensing and eHealth training recommendations. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 627–635. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_63
Acknowledgements
The authors would like to thank their colleagues from ACC Cyfronet Marek Kasztelnik, Maciej Malawski, Jan Meizner and Piotr Nowakowski for their help in understanding the structure of the Atmosphere platform and for valuable discussions and suggestions regarding the Kubernetes. The work was carried out during the GJP’s stay at AGH under the Erasmus+ program. MB research was partially supported by EU H2020 grant Sano 857533 and IRAP FNP project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pezzullo, G.J., Di Martino, B., Bubak, M. (2022). Container-Based Platform for Computational Medicine. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-99619-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99618-5
Online ISBN: 978-3-030-99619-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)