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.

Implementing high-performance computing (HPC) to solve problems in energy infrastructure resilience research in a heterogeneous environment based on an in-memory data grid (IMDG) presents a challenge to workflow management systems. Large-scale energy infrastructure research needs multi-variant planning and tools to allocate and dispatch distributed computing resources that pool together to let applications share data, taking into account the subject domain specificity, resource characteristics, and quotas for resource use. To that end, we propose an approach to implement HPC-based resilience analysis using our Orlando Tools (OT) framework. To dynamically scale computing resources, we provide their integration with the relevant software, identifying key application parameters that can have a significant impact on the amount of data processed and the amount of resources required. We automate the startup of the IMDG cluster to execute workflows. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our solution allows us to investigate large infrastructures by modeling multiple simultaneous failures of different types of elements down to the number of network elements. In terms of task and resource utilization efficiency, we achieve almost linear speedup as the number of nodes of each resource increases.

Nowadays, scientific applications based on a workflow are actively used. Such applications are one of the forms of distributed applied software packages. The modern tools for designing packages do not fully support continuous integration for both the system and applied software for a heterogeneous distributed computing environment. The software quality, its design time, and experiment conduct time can be improved through expanding the scope of continuous integration for packages. To this end, we provide a new approach to ensuring the continuous integration for both the applied and system software within package designing. In practice, the approach applying enables us to significantly decrease in the number of errors and failures when packages are designed and used. Thus, we substantially reduce the time of conducting computing experiments in a heterogeneous distributed computing environment and increase the efficiency for the use of its resources.

Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Often, data processing requires the utilization of high-performance computing. To this end, the paper addresses a new approach to automation of implementing resource-intensive computational operations of web processing services in a heterogeneous distributed computing environment. To implement such an operation, we develop a workflow-based scientific application executed under the control of a multi-agent system. Agents represent heterogeneous resources of the environment and distribute the computational load among themselves. Software development is realized in the Orlando Tools framework, which we apply to creating and operating problem-oriented applications. The advantages of the proposed approach are in integrating geographic information services and high-performance computing tools, as well as in increasing computation speedup, balancing computational load, and improving the efficiency of resource use in the heterogeneous distributed computing environment. These advantages are shown in analyzing multidimensional time series.

We offer a new logical method for a parametric synthesizing linear static regulator in binary dynamic systems. To describe the dynamics in the general case of a nonlinear object and linear regulator state equations are used. The required dynamics property (in our case the property of stability) specification of a closed system is given in the formal logic language. In this formulation, the synthesis problem is reduced to the qualitative analysis problem - the verification of the truth of the quantified Boolean formula with the simultaneous search for the feedback matrix. The proposed method allows the natural parallelization based on data and high scalability with increasing the problem dimension in case of it is implemented in a high-performance computing environment. We develop a service-oriented solver, including the tools intended to automatically create a Boolean model of the original problem and solve the synthesis problem of a linear regulator using this model.