This article is dedicated to ecological monitoring of Siberia. The regional features of environmental problems are presented, and an integrated approach to the organization of digital monitoring as a tool for understanding what is happening and its forecasting is proposed. An approach to the digitalization of environmental monitoring based on digital platforms, which provide the collection, storage, and processing of large amounts of data in a distributed network of sensors, and satellite information, as well as services for modeling and forecasting, is considered. The results of digital monitoring in the Baikal natural territory are presented. This article is based on a report heard at the RAS Presidium meeting on June 22, 2021.

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.

A new approach to creating a domain-specific heterogeneous distributed computing environment is considered. It is used to support decision-making on urgent problems of increasing the survivability of energy systems. The approach is based on the use of high-performance computing, multiagent scheduling of computations and resource assignment, tools for processing semistructured information, and visualization of subject data using electronic maps. Decision-making alternatives are evaluated using combinatorial modeling and multicriteria optimization. Orlando Tools is used as the base for the environment’s integrated software. It implements flexible modular construction of scalable scientific applications (distributed application packages). The advantages of using the environment are demonstrated by the example of solving practical problems.

The approach is based upon declarative specifications of database applications (SDA). The specifications of database applications contain all the information about database structure, which is required to build a typical AIS (automated information system). The information is represented in its pure form, so the specifications are rather concise. The AIS'es are implemented using general algorithms, which are directed by the specifications. We have developed algorithms for such tasks as: user interface generation, query building, report generation, GIS interaction. Using the specifications of database applications and the algorithms the software system GeoARM was implemented. The technology considered was well-tried by use of the system GeoARM for development of several dozens of true-life AIS for different purposes.

The paper addresses an important problem related to automation of simulation modeling of queue systems using high-performance computing. There is a large spectrum of tools for the simulation modeling of such systems. However, these tools often do not use the possibilities of subject-oriented high-performance computing in full. The purpose of the study is the development of the new tools for automation of the simulation modeling of queue systems in a heterogeneous distributed computing environment. To this end, we develop specialized applications for parameter sweep computations in such an environment. A methodology of the application development is based on the integrated use of methods of the conceptual and wireframe programming, methods of the computations planning and resources allocation, methods of parameter sweep computations, and methods of the data analysis. We develop the toolkit named SIRIUS III for the simulation modeling of queue systems using high-performance computing. In addition, we design a conceptual model that represents knowledge about both the subject domain of solved problems and computing environment. Using the proposed toolkit, we solved a number of problems related to warehouse logistics. Computational experiments showed the high scalability of parameter sweep computations on heterogeneous resources.

We propose a new approach to creating a subject-oriented distributed computing environment. Such an environment is used to support decision-making in solving relevant problems of ensuring energy systems resilience. The proposed approach is based on the idea of advancing and integrating the following important capabilities in supercomputer engineering: continuous integration, delivery, and deployment of the system and applied software, high-performance computing in heterogeneous environments, multi-agent intelligent computation planning and resource allocation, big data processing and geo-information servicing for subject information, including weakly structured data, and decision-making support. This combination of capabilities and their advancing are unique to the subject domain under consideration, which is related to combinatorial studying critical objects of energy systems. Evaluation of decision-making alternatives is carrying out through applying combinatorial modeling and multi-criteria selection rules. The Orlando Tools framework is used as the basis for an integrated software environment. It implements a flexible modular approach to the development of scientific applications (distributed applied software packages).