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Abstract The modern datacenter's computing capabilities have far outstripped the applications running within and have become a hidden cost of doing business due to how software is architected and deployed. Resources are over-allocated to monolithic applications that sit idle for large parts of the day. If applications were architected and deployed differently, shared services could be used for multiple applications as needed. When combined with powerful orchestration software, containerized microservices can both deploy and dynamically scale applications from very small to very large within moments—scaling the application not only across a single datacenter but across all datacenters where the application(s) are deployed. In this paper, we analyze data from an application(s) deployed both as a single monolithic codebase and as a containerized application using microservice-based architecture to calculate the performance and computing resource waste are both architected and deployed. A modern approach is offered as a solution as a path from how to go from a monolithic codebase to a more efficient, reliable, scalable, and less costly deployment model.

The modern datacenter's computing capabilities have far outstripped the applications running within and have become a hidden cost of doing business due to how software is architected and deployed. Resources are over-allocated to monolithic applications that sit idle for large parts of the day. If applications were architected and deployed differently, shared services could be used for multiple applications as needed. When combined with powerful orchestration software, containerized microservices can both deploy and dynamically scale applications from very small to very large within moments—scaling the application not only across a single datacenter but across all datacenters where the application(s) are deployed. In this paper, we analyze data from an application(s) deployed both as a single monolithic codebase and as a containerized application using microservice-based architecture to calculate the performance and computing resource waste are both architected and deployed. A modern approach is offered as a solution as a path from how to go from a monolithic codebase to a more efficient, reliable, scalable, and less costly deployment model. [ABSTRACT FROM AUTHOR]

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.