Effective solving complex mathematical modeling problems is based on the use of high-performance computing. Clouds, grids, and public access supercomputer centers are commonly used platforms. Their integration into a unified environment provides possibilities for carrying out mass large-scale scientific experiments and efficient scalable resource allocation at different stages of the application design and execution. However, end-users have to carefully select optimization criteria such as completion time, deadlines, reliability, cost, etc. It is a complicated problem due to integrated resources differ significantly in their computing capabilities, hardware and software platforms, system architectures, user interfaces, etc. The paper presents new features of the Orlando Tools framework for the development of distributed applied software packages (scalable scientific applications) that mitigates various types of uncertainties arising from the job distribution in the integrated computing environment. It provides continuous integration, delivery, and deployment of applied and system software to significantly mitigate the negative impact of uncertainty on problem-solving time, computation reliability, and resource efficiency. An experimental analysis of the sustainable design and development of the real energy sector clearly demonstrates the advantages of the tools.

Nowadays, simulation modeling is a relevant and practically significant means in the field for research of infrastructure object functioning. It forms the basis for studying the most important components of such objects represented by their digital twins. Applying meteorological data, in this context, becomes an important issue. In the paper, we propose a new microservice-based approach for organizing simulation modeling in heterogeneous distributed computing environments. Within the proposed approach, all operations related to data preparing, executing models, and analyzing the obtained results are implemented as microservices. The main advantages of the proposed approach are the parameter sweep computing within simulation modeling and possibility of integrating resources of public access supercomputer centers with cloud and fog platforms. Moreover, we provide automated microservice web forms using special model specifications. We develop and apply the service-oriented tools for studying environmentally friendly equipment of the objects at the Baikal natural territory. Among such objects are recreation tourist centers, children’s camps, museums, exhibition centers, etc. As a result, we have evaluated the costs for the possible use of heat pumps in different operational and meteorological conditions for the typical object. The provided comparative analysis has confirmed the aforementioned advantages of the proposed approach.

We address dynamic elasticity issues of VM provisioning in a heterogeneous distributed computing environment that integrates resources of a data center. We consider the scenario when the center includes dedicated resources (private cloud) for providing virtualized service and non-dedicated resources for Grid-computing. Existing platforms for the resource virtualization does not support management of such an infrastructure. We propose an approach to a job management based on the dynamic elasticity of virtual machines provisioning using resources of both types. We develop the multi¬agent job scheduler for dedicated resources and hypervisor shell to launch virtual machines through queues of resource management systems in non-dedicated resources. The scheduler provides a dynamic elasticity of virtual machine provisioning. Advantages of the offered approach to the resource virtualization are demonstrated by an example of a job flow management for a scalable application to solve the complicated practical problem. It is related to the energy security of Vietnam. Provided experiments show that using the developed tools together with the platform for the resource virtualization enables agents to significantly speed up the problem-solving process.