# Railway Cloud: Management and Orchestration Functionality Designed as Microservices
## 2 Related Works
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Area** & **References** & **Considered Problems** \\ \hline Railway edge clouds & [26; 27; 28; 29] & Optimization of offloading real-time control on edge clouds \\ Cloud computing platform architectures & [30; 31; 32; 33; 34; 35; 36] & Resilient and secure frameworks embedding AI and intelligent workflows, virtualization technologies \\ Cloud-based railway applications & [37; 38; 39; 40; 41] & Automatic train supervision, track monitoring, fault diagnostics, track surveillance, secure communications, ticketing systems \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of works concerning problems related to the railways cloud aspects. The management of industrial clouds comes with a set of problems related to data security and privacy, performance and reliability, cloud migration, interoperability, optimisation of management costs, required skills, and knowledge. In [50], the authors provide a study on industrial multi-cloud management platforms, analysing the difficulties and challenges of building such architectures. In [51], innovative approaches to resource allocation and performance optimisation techniques in a multi-cloud environment are explored. A blockchain-based multi-cloud infrastructure that unifies the management of clouds provided by different vendors is presented in [52]. In [53], the principles of cloud management required for optimal cloud resource allocation and the migration of virtual machines are stressed. In [54], the authors provide a survey on cloud resource management by comparing different cloud computing algorithms. Problems related to the balance between cloud performance and power consumption and existing management approaches are discussed in [55]. In [56], the author proposes a hybrid approach to secure cloud resource allocation based on machine learning and task scheduling. A cloud resource management strategy is discussed in [57], which is an auction mechanism and improves traditional resource allocation strategies. The problems related to data recovery and backup in cloud computing are discussed in [58]. In [59], the authors present a review of the research in cloud computing security, point out threats and challenges, and propose ideas for resolving cloud data integrity. Based on the analysis of existing works on cloud computing security, in [60], the authors identify associated challenges and propose some recommendations. In [61], a layered security performance management model is studied. In [62], the authors propose a method for the estimation of access to cloud data and a new security access control scheme. Table 2 summarises the latest research on cloud management. The variety of works conducted on railway edge clouds and cloudified railway applications do not concern the interfaces between clouds and the rest part of the railway control system. They focus on control functions that could be cloudified and on the benefits of user proximity. The mechanisms of interaction between edge rail applications and managed railway objects are not refined. Works related to cloud computing platform's architectures consider resilient and secured frameworks that make it possible to embed intelligent and virtualised functions which stress reliability, safety, and security requirements. Railway cloud management, being a unified, open, and multi-vendor solution, seems to be an open research question. The existing research related to industrial clouds focuses on multi-cloud interoperability and cloud resource management issues. The proposed cloud management architectures provide high-level views without elaborating on management functionality, generic cloud management services, management functions, and numerous elements. Furthermore, the interworking between cloud services management and controlled components, as a complex of interfaces and procedures, is left without discussion or in-depth typical use cases.