# The FaaS-Based Cloud Agnostic Architecture of Medical Services--Polish Case Study
## 2 Related Work
Since the process of diagnosing patients is primarily data-driven, it is the issue of medical data storage that makes up a large share of the overall scientific interest in the health sector. For example, based on a case study of storage services to manage medical imagery, the authors of [17] describe research which results in the development of a storage mesh architecture to create and operate reliable, configurable and flexible serverless storage services for heterogeneous infrastructures. The topic of the distributed file system is also taken up by the authors of the article [18] which proposes a robust, available, scalable and serverless solution structure for storing large amounts of data in the medical field. Big data sets also pose challenges related to their efficient and effective searching. Here, too, researchers [19] refer to the concept of serverless computing. They develop the ScanMedicine module which is a new searching system dedicated to providing healthcare professionals, patients and researchers with easy access to the intelligence underpinning health technology innovations. Once again, the serverless part of the architecture assumes the use of AWS Lambda services. Proposals for serverless solutions can also be found in research on telemedicine-like solutions. An example of this is a chatbot developed in India that helps people potentially suffering from COVID-19 to find a vacancy in the hospital. However, for its development, the authors of [20] do not use the classic FaaS approach, in which they could imperatively program logic, but use a number of services from the Microsoft cloud, such as Power VirtualAgent or Power Automate. Another chatbot proposal [21], however, also created to limit the spread of COVID-19, uses a more generic FaaS solution, namely Firebase Cloud Functions. Serverless computing is also applicable to more common medical transactional applications. A good example is the proposed system in [22] to support the delivery of drugs to Indian villages. Although its architecture is documented only briefly, the authors declare the implementation of backend services using AWS Lambda and Amazon API Gateway. Serverless computing can be of particular interest in event-driven data processing. This is undoubtedly the case in WBANS class systems (wireless body area networks). In their publication [23], the authors propose a system architecture for such applications, the backend part of which is located in the computing cloud. They compare the services that would be appropriate for the development of such a system from both the AWS and Azure clouds and ultimately decide on the first one using, inter alia, the AWS Lambda service. A solution also belonging to the WBAN class, implemented as a mobile application for monitoring the heart rate, presented in another paper [24], uses Google Cloud services, in particular Firebase Cloud Functions. Last but not least, serverless computing can be applied for deep learning issues, which was presented in the example of medical services for patient-specific arrhythmia detection [25]. According to the authors, using AWS Lambda makes the serverless setup orders of magnitude cheaper and more scalable than a dedicated host running 24/7. As can be seen from the above review of the state of research on the use of serverless computing in the field of healthcare, most applications concern cloud functions, which means they use services provided by a specific cloud provider (most often it is AWS Lambda). The authors of this paper decide to take a slightly different direction, namely to use a solution based on the Kubernetes cluster to be more in line with the cloud-agnostic approach. This is to avoid so-called vendor lock-in to a specific computing cloud and thus enable its easy implementation at various cloud computing providers, without the need for major changes in the proposed architecture of the solution.