Railway Cloud Resource Management as a Service
Cloud computing has the potential to accelerate the digital journey of railways. Railway systems are big and complex, involving a lot of parts, like trains, tracks, signaling systems, and control systems, among others. The application of cloud computing technologies in the railway industry has the potential to enhance operational efficiency, data management, and overall system performance. Cloud management is essential for complex systems, and the automation of management services can speed up the provisioning, deployment, and maintenance of cloud infrastructure and applications by enabling visibility across the environment. It can provide consistent and unified management over resource allocation, streamline security processes, and automate the monitoring of key performance indicators. Key railway cloud management challenges include the lack of open interfaces and standardization, which are related to the vendor lock-in problem. In this paper, we propose an approach to design the railway cloud resource management as a service. Based on typical use cases, the requirements to fault and performance management of the railway cloud resources are identified. The main functionality is designed as RESTful services. The approach feasibility is proved by formal verification of the cloud resource management models supported by cloud management application and services. The proposed approach is open, in contrast to any proprietary solutions and feature scalability and interoperability.
Authors
- Ventsislav Trifonov (ORCID: https://orcid.org/0000-0001-7604-2205)
- Dragomira Dimitrova
- Ivaylo Atanasov (ORCID: https://orcid.org/0000-0002-6938-516X)
- Evelina Pencheva (ORCID: https://orcid.org/0000-0002-5909-2638)
Institutions
- Technical University of Sofia (BG)
- Todor Kableshkov University of Transport (BG)
Publication Details
- Journal
- Future Internet
- Published
- 2025-04-24
- DOI
- https://doi.org/10.3390/fi17050192
- Citations
- 7
- Primary Topic
- Advanced Research in Systems and Signal Processing
- Type
- article
- Field-Weighted Citation Impact
- 8.50
Funders
- Bulgarian National Science Fund