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Introduction. A brief overview of the properties and architecture of one of the components of the National Cloud of Open Science prototype – the cloud platform OpenStack is given. The list of software and hardware components of the OpenStack test cloud environment and the sequence of actions required for the deployment of both OpenStack itself and the Slurm virtual cluster environment for portable, scalable, reproducible scientific biomedical computing are presented. The purpose of the paper is a description of the experience of test deployment of OpenStack to create a scalable computing environment for reproducible scientific computing using modern technological solutions, which can be applied to both cloud (OpenStack, AWS, Google) and cluster platforms (Slurm). Results. The structure of the created test containerized (using Singularity technology) biomedical application, which contains modern software and libraries and can be used in conventional and cloud virtual cluster environments is briefly described. The results of a comparative test of this application in the virtual cluster environment Slurm under the control of OpenStack and in the node of cluster SKIT-4.5 in the V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine are given. Information on solving the problem of finding the optimal in terms of saving resources scaling parameters for the developed application in two comparable cluster environments is given. Some features of the use of these cluster environments are clarified, in particular, a comparison of the dependence of the application speed on the number of parallel processes for two cluster environments is presented. Empirical data are presented in graphical form, which illustrate the nature of the load on the OpenStack server and the use of RAM on the number of parallel processes. Possibilities of portability between the specified cluster environments, scaling of calculations and maintenance of reproducibility of calculations for the offered test application are demonstrated. The advantages of using OpenStack technology for scientific biomedical calculations are pointed out. Conclusions. The described example of test deployment and use of OpenStack gives an idea of the requirements for the necessary technical base to ensure the reproducibility of scientific biomedical calculations in cloud and cluster environments.

Introduction. This publication summarizes the experience of the use of applied containerized software tools in cloud environment, which the authors gained during the project “Development of methods, algorithms and intellectual analytical system for processing and analysis of heterogeneous clinical and biomedical data in order to improve the diagnosis of complex diseases”, accomplished by the team from the United Institute of Informatics Problems of the NAS of Belarus and V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine. In parallel, the features of biomedical data and the main approaches to their processing and classification, implemented within the framework of an intelligent analytical system, and the possibility of their implementation as part of a container application are described. The purpose of the paper is to describe modern technologies that ensure the reproducibility of numerical experiments in this field and the tools aimed to integrate several sources of biomedical information in order to improve the diagnostics and prognosis of complex diseases. Special attention is also paid to the methods of handling data received from different sources of biomedical information. Particular attention is paid to methods of processing data obtained from various sources of biomedical information and included to the intelligent analytical system. Results. The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. The developed hybrid classification model presents the basis of the intelligent analytical system and aims to integrate several sources of biomedical information. Conclusions. The experience of using the developed classification module NonSmoothSVC, which is part of the developed intelligent analytical system, gained during its testing on artificial and real data, allows us to conclude about several advantages provided by the containerized form of the created application. Namely: • It permits to provide access to real data located in cloud environment, • It is possible to perform calculations to solve research problems on cloud resources both with the help of developed tools and with the help of cloud services, • Such a form of research organization makes numerical experiments reproducible, i.e. any other researcher can compare the results of their developments on specific data that have already been studied by others, in order to verify the conclusions and technical feasibility of new results, • There exists a universal opportunity to use the developed tools on technical devices of various classes from a personal computer to powerful cluster. The hybrid classification model as a core of the intelligent system will make it possible to integrate multidimensional, heterogeneous biomedical data with the aim to better understand the molecular courses of disease origin and development, to improve the identification of disease subtypes and disease prognosis.