# Railway Cloud Resource Management as a Service
## 1 Introduction  
Nowadays, railway operators are striving to improve their services in competition with other forms of transport. The aim is to increase the capacity of trains to carry more passengers and freight in response to rising traffic demand, and to provide a better travel experience, while ensuring a higher level of safety and security. Integrating cloud computing into railways could enhance efficiency, maintenance, and operations by leveraging scalable, remote, and data-driven solutions. Typical cloud-based applications include maintenance and predictive analytics related to railway infrastructure and rolling stock [1, 2], railway signaling and traffic management [3, 4, 5], internet of things integration [6, 7, 8], big data analytics and railway planning [9, 10], Artificial Intelligence (AI) for operation optimization [11, 12], and cloud-based ticketing and passenger management [13, 14]. By adopting cloud technology, the digital railways become more adaptive while maintaining high safety requirements. Adaptive railway systems utilize a higher level of automation to control train speed and the transportation of passengers and goods, reducing travel time and increasing efficiency and competitiveness. Furthermore, cloud-based solutions can provide accurate real-time data of rail operations to detect potentialdangers and anomalies, and enable useful insights for optimal monitoring and control. Operational data reflects the state of physical assets, such as train location and speed, train door conditions, track temperature, and weather data. The data is gathered by IoT sensors onboard the train and alongside the tracks. The data are sent to the cloud for further processing and are consumed by different cloud applications. Operational data and analytic applications enable real-time monitoring and control, and thus improve service reliability. Essentially, the railway cloud is a segregated pool of computing and storage resources dedicated to run mission critical applications and to store associated data. Usually, it is hosted in private data centers. Due to the highly critical nature of the operational technology applications, the essential railway cloud attributes include strong resilience with five nines of high availability, deterministic quality of service (QoS), robust network security, distributed architecture, and edge computing integration. Safety critical railway applications, which are responsible for controlling and monitoring the train systems, are highly sensitive to communication disruptions and may increase the risk of potential safety hazards; so, it is essential to have robust connectivity to preserve mission critical grade availability [15, 16]. Strong and flexible QoS capabilities include railway application awareness and prioritization, which ensure good application performance, reduce congestion issues, and protect the network from server misconfiguration [17]. Railways handle sensitive data, so securing the cloud environment is critical to protect against cyber threats. Ensuring compliance with data protection regulations is essential when storing and processing passenger and operational data in the cloud [18, 19]. Railway networks are geographically dispersed, so the cloud infrastructure must support distributed computing across multiple regions or locations [20]. Railway operations often involve edge computing to process data locally at stations, trains, or tracks before sending it to the cloud [21, 22, 23]. In this paper, a service-based approach to railway cloud resource management is proposed. The focus is on the cloud orchestration functions related to fault and performance management.