# Railway Cloud Resource Management as a Service
## 2 Related Works
In [39], the authors outline the security issues that cloud computing raises, and suggest solutions that safeguard private information and systems in cloud-based environments for businesses. In [40], the author provides general guidelines on auditing standards by referring to threats and vulnerabilities, and suggests a unified approach toward audit considerations in cloud computing environment. * _Monitoring and analytics._ Integration of cloud orchestration with monitoring and analytics platforms provides real-time insights into the performance of the cloud infrastructure. It is aimed at automatic resource adjustment based on usage patterns and alerts from monitoring tools. The tutorial presented in [41] discusses the AI techniques that can help in fault and performance management in multi-cloud virtual network. In [42], the authors present an application anomaly detection and bottleneck identification system based on cloud platform service components, that can monitor and analyze applications on multi-layered cloud platforms with customized index values. Studies on the platform design of distributed cloud monitoring and the key technologies of big data storage can be found in [43]. The survey presented in [44] provides an overview and analysis of advanced techniques for anomaly detection and localization of cloudified multi-service applications. * _Container management_. Orchestration tools, like Kubernetes, can manage containerized applications, ensuring high availability and efficient resource utilization. It is also referred to as automatically scaling of container instances based on application demand. The authors of [45] discuss and compare emerging container platforms and cloud-centric orchestration frameworks, highlighting the challenges involved. * _Microservices architecture._ Microservices are a powerful architectural paradigm for creating and deploying contemporary applications in the cloud computing environment. They can be managed and scaled independently. Microservices management must ensure service discovery and load balancing across distributed systems. In [46], the authors compare the features and constraints of several cloud platforms and tools for deploying and orchestrating microservices. A comprehensive overview of microservices as a suitable complementation of cloud computing is provided in [47], where the authors outline their technical challenges, such as performance, debugging, and data consistency. In our previous research, the basic concepts related to the railway cloud orchestration and management are defined, and the requirements for its basic orchestration are identified [48]. The management interface provided by the railway cloud platform (RCP) to the Railway Management Automation and Orchestration (RMAO) platform includes two types of services:  
* _Cloud resource management (CRM)_ services that orchestrate railway cloud lifecycle processes, and are responsible for the allocation and delivery of cloud resources and resource management software, including cloud deployment services, cloud infrastructure catalogue services, railway cloud monitoring services, railway cloud provisioning services, etc. * _Cloudified function management (CFM)_ services that are responsible for the management of the lifecycles of the cloudified railway functions deployed on the railway cloud. The basic orchestration of the railway cloud is related to the railway cloud's provisioning. The provisioning of the railway cloud includes the railway cloud's deployment and the integration of cloud services within the railway infrastructure. It assumes that the basic infrastructure (e.g., physical resources) is built and installed, and that secured connectivity between the components of the intelligent railway control systems exists. In [48], our focus was on the deployment of railway cloud services and resource allocation. An approach to design of functions related to the CFM services' software update as a service was proposed.