# The FaaS-Based Cloud Agnostic Architecture of Medical Services--Polish Case Study
## 2 Related Work  
For some time now, we have been witnessing an industrial revolution driven by the development of technology. The healthcare field is part of this industry and its development is not at all lagging behind. Based on successive paradigms, there is now talk of Industry 4.0 and 5.0 [5], and scientific research on the development of healthcare in these areas is confirmed by numerous publications [6]. Among the many developments in IT, technologies such as IoT, blockchain, AI and cloud computing have become key drivers in recent years. This is also happening in the healthcare field [7; 8; 9]. It has been less than eight years since AWS--one of the biggest players in cloud computing--publicly released the first FaaS product. Since then, the popularity of this model of conducting computations has gained many followers, found many practical applications and become the subject of research conducted by many researchers. Quite surprisingly, however, it seems that relatively few practical applications or research papers are related to the healthcare sector, even broadly defined. Serverless computing applications in the healthcare sector can be roughly divided into several application areas. A strong group here is bioinformatics computing. For example, one of the biggest challenges in the field of omics computing is the data sequencing process itself, which is computationally very demanding. Executing such computations in a standard model requires the reservation of computing power and the self-management of virtual servers. However, research shows [10] that, using AWS Lambda, it is possible to conduct the all-against-all pairwise comparison among all unique human proteins in approximately 2 min, at a cost of less than USD 1, while performing the same task on a typical laptop computer would take more than 8 h. An interesting application of serverless computing is shown in paper [11], where building a high-quality annotation corpus (MedTator) for biomedical and clinical research applications is described. Due to involving a serverless approach, the authors develop the application with an intuitive and interactive user interface that focuses on the core steps related to corpus annotation, such as document annotation, corpus summarization, annotation export and annotation adjudication. In other studies [12], the authors identify bottlenecks that stand in the way of efficient omics data processing. In their view, it seems to become problematic to store data, preprocess them or integrate data from multiple sources. To streamline the computational process, they propose the use of the Amazon Serverless Lambda service and discuss the benefits but also the risks of doing so. More information on applications of the FaaS model for bioinformatics computing can be found in a dedicated literature review [13,14]. Another area of application in serverless computing, characterized by the possibility of batch data processing, is the analysis of medical data, in particular imaging diagnostics. Based on it, the authors of [15] present a serverless model for highly parallel file processing applications. They also describe a middleware implementation that supports the execution of customized execution environments based on Docker images on AWS Lambda. Their results prove that the combination of a high-level programming model with the scalable capabilities of AWS Lambda makes it easy for end-users to efficiently exploit serverless computing for the optimized and cost-effective execution of loosely coupled tasks. Other research [16] shows the concept of the serverless edge data analytics platform and application model and discuss its main design requirements and challenges, based on real-life healthcare use case scenarios. The authors hope that the proposed model will switch the current view of centralized premise and cloud real-time analytics into more distributed, edge, ubiquitous, real-time analytics, in which the data's value will not be lost at the edge and all computing layers will be used evenly.