In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations.

In this article, the authors, using information-systems modeling techniques, and considering current national legal regulations, present the cloud-enabled architecture of a clinical data repository. The patient’s medical record is an important carrier of information necessary for accurate diagnosis and selection of the correct treatment process. Therefore, it is not surprising that since the beginning of the development of computer technologies, databases have been built to enable the management of a patient’s medical records. These systems were most-often deployed locally at individual healthcare units, which carried certain limitations both in terms of the security and availability of the stored information, and the possibility of exchanging it with other clinics. However, recent developments in the standardization of medical information exchange in Poland, together with the revolution in cloud computing, have opened up completely new perspectives for clinical-data-repository implementations helping to make them far more sustainable. Although, the practical aspects of implementing clinical-documentation repositories are studied both in forums of European countries and also around the world; so far, no similar research was conducted with respect to Poland. This study tries to fill that gap by proposing a flexible multi-variant cloud-enabled architecture of the system providing the services of a clinical-data repository. The goal of the work was to propose such a system architecture that allows having a system that is either cloud-agnostic, that uses specifically selected cloud services, or that is even deployable locally. Thanks to the use of cloud computing services, the implemented system is characterized by high availability, scalability, and the possibility of exchanging data between medical institutions, which enables the improvement in the quality of medical processes for the whole Polish population.

In this paper, the authors, based on a case study of the Polish healthcare IT system being deployed to the cloud, show the possibilities for limiting the computing resources consumption of rarely used services. The architecture of today’s developed application systems is often based on the architectural style of microservices, where individual groups of services are deployed independently of each other. This is also the case with the system under discussion. Most often, the nature of the workload of each group of services is different, which creates some challenges but also provides opportunities to make optimizations in the consumption of computing resources, thus lowering the environmental footprint and at the same time gaining measurable financial benefits. Unlike other scaling methods, such as those based on MDP and reinforcement learning in particular, which focus on system load prediction, in this paper, the authors propose a reactive approach in which any, even unpredictable, change in system load may result in a change (autoscaling) in the number of instances of computing processes so as to adapt the system to the current demand for computing resources as soon as possible. The authors’ main motivation for undertaking the study is to observe the growing interest in implementing FaaS technology in systems deployed to production in many fields, but with relatively little adoption in the healthcare field. Thus, as part of the research conducted here, the authors propose a solution for infrequently used services enabling the so-called scale-to-zero feature using the FaaS model implemented by the Fission tool. This solution is at the same time compatible with the cloud-agnostic approach which in turn helps avoid so-called cloud computing vendor lock-in. Using the example of the system in question, quantitative experimental results showing the savings achieved are presented, proving the justification for this novel implementation in the field of healthcare IT systems.