Е-Публикации
Технически университет - София

Abstract: Cloudification and virtualization of railway operations and management have a great potential to optimize its performance and to improve reliability, safety, and security of railway services. In authors’ previous research, an intelligent railway control system architecture is proposed which features distributed intelligence and benefits from cloudified services. The main architectural components of the proposed architecture include Railway Management Automation and Orchestration platform, Intelligent Railway Controller, and Railway cloud. In this paper, the focus is on the management and orchestration functions of the Railway cloud. The paper main points include identification of the requirements to the railway cloud management and orchestration, synthesis of orchestration functionality as microservices by identification of service resources, and modeling the microservice and orchestration application logic. In contrast to any proprietary solutions in the highly competitive market, the approach is based on open interfaces to orchestrate railway cloud services, that could be improved over time and tailored to specific needs.

References

1. Li, Y., Zhu, L.: Collaborative cloud and edge computing in 5g based train control systems. In: Global Communications Conference (GLOBECOM), pp. 2542–2547, IEEE, Rio de Janeiro, Brazil (2022). https://doi.org/10.1109/GLOBECOM48099.2022.10001291
2. Zhu, L., Zhuang, Q., Jiang, H., Liang, H., Gao, X., Wang, W.: Reliability-aware failure recovery for cloud computing based automatic train supervision systems in urban rail transit using deep reinforcement learning. J. Cloud Comput.: Adv., Syst. Appl. 12(147), 1–14 (2023). https://doi.org/10.1186/s13677-023-00502-x
3. Wang, Q., Chai, M., Wang, H., Zhang, H., Chai, J., Lin, B.: Cloud-based simulated automated testing platform forvirtual coupling system. In: 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 2738–2743, IEEE, Macau, China (2022). https://doi.org/10.1109/ITSC55140.2022.9922450
4. Gala, G., Fohler, G., Tummeltshammer, P., Resch, S., Hametner, R.: RT-cloud: virtualization technologies and cloud computing for railway use-case. In: 24th International Symposium on Real-Time Distributed Computing (ISORC), pp. 105–113, IEEE, Daegu, South Korea (2021). https://doi.org/10.1109/ISORC52013.2021.00024
5. Wang, S., Hussin, N., Yang, J., Yang, X.: Design and implementation of an industry data analysis model based on cloud computing. In: 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), pp. 81–84, IEEE, Nanjing, China (2023). https://doi.org/10.1109/ISCEIC59030.2023.10271170
6. Aufderheide, H., Brandes, N.: Architecting for Resilience in the cloud for critical railway systems. AWS for Industries (2023). https://aws.amazon.com/blogs/industries/architecting-for-resilience-in-the-cloud-for-critical-railway-systems-2/. Accessed 26 Dec 2023
7. Huang, Q., Yin, W., An, J., Zhou, Y.: A China railway express-based model for designing a cross-border logistics information cloud platform scheme. Appl. Sci. 10(12), 4110 (2020). https://doi.org/10.3390/app10124110
8. Yu, S.-D.: Research on cloud computing in the key technologies of railway intelligent operation and maintenance sharing platform. J. Phys: Conf. Ser. 1800(1), 1–9 (2021). https://doi.org/10.1088/1742-6596/1800/1/012010
9. Yu, S., Chang, H., Wang, H.: Design of cloud computing and microservice-based urban rail transit integrated supervisory control system plus. Urban Rail Transit 6, 187–204 (2020). https://doi.org/10.1007/s40864-020-00138-z
10. Zhang, X.: Optimization design of railway logistics center layout based on mobile cloud edge. PeerJ Comput. Sci. 9, e1298 (2023). https://doi.org/10.7717/peerj-cs.1298
11. Pencheva, E., Atanasov, I., Trifonov, V.: Towards intelligent, programmable, and open railway networks. Appl. Sci. 12(8), 117–124 (2022). https://doi.org/10.3390/app12084062
12. Atanasov, I., Vatakov, V., Pencheva, E.: A microservices-based approach to designing an intelligent railway control system architecture. Symmetry 15(8), 1566 (2023). https://doi.org/10.3390/sym15081566
13. Sangiorgi, D.: Introduction to Bisimulation and Conduction. 1st ed., Cambridge University Press (2011). https://doi.org/10.1017/CBO9780511777110
14. Koutavas, V., Lin, Y.-Y., Tzevelekos, N.: Fully abstract normal form bisimulation for call-by-value PCF. In: 38th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), pp. 1–13, IEEE/ACM, Boston, MA, USA (2023). https://doi.org/10.1109/LICS56636.2023.10175778

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