Advanced Information, Computation, and Control Systems for Distributed Environments II

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6343

Special Issue Editors


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Guest Editor
Matrosov Institute for System Dynamics and Control Theory of the Siberian Branch of the Russian Academy of Sciences, Lermontov St. 134, 664033 Irkutsk, Russia
Interests: artificial intelligence; geoinformation systems; web-technologies; mathematical modeling; cloud computing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. CICESE Research Center, Carr Tijuana-Ensenada 3918, Zona Playitas, Ensenada 22860, Mexico
2. Ivannikov Institute for System Programming of the Russian Academy of Sciences, Alexander Solzhenitsyn st., 25. 109004 Moscow, Russia
3. Problem-Oriented Cloud Computing Environment International Laboratory, South Ural State University, Prospekt Lenina 76, 454080 Chelyabinsk, Russia
Interests: grid and cloud; multiobjective resource optimization; security; uncertainty; scheduling; heuristics and meta-heuristics; adaptive resource allocation; Internet of Things
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Matrosov Institute for System Dynamics and Control Theory of the Siberian Branch of the Russian Academy of Sciences, Lermontov St. 134, 664033 Irkutsk, Russia
Interests: computational models; applied software packages; knowledge engineering; distributed computing; multi-agent technologies; simulation modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The International Workshop on Information, Computation, and Control Systems for Distributed Environments (ICCS-DE 2023) will be held in Irkutsk, Russia (July 3-7, 2023), https://iccs-de.icc.ru/. The workshop organizers are the Matrosov Institute for System Dynamics and Control Theory of the Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia and the CICESE Research Center, Ensenada, Mexico.

Today, the digitalization of various aspects of our life and work is becoming a reality. This is based on the implementation and use of a large spectrum of software systems for various purposes. However, the architectures and concepts of many of them are based on technologies that have evolved to varying degrees over the past several decades. Therefore, they often reach the limits of their capabilities when applied in the conditions of modern digitalization and do not provide the quality use of sophisticated data management to enable multiparty participation in various activities.

In this regard, we provide this Special Issue of the journal Computation that addresses the theory and practice in developing advanced information, computing, and control systems related to different subject domains of information, technical, economic, and other fields of human activity. The benefits of using advanced systems are multifold. Advanced systems affect the acceleration and increase in the reliability of data processing, quality and timeliness of intelligence and decision-making, improvement of service of end-users, financial performance, etc. In general, this significantly increases the competitiveness of subjects that actively apply advanced information, computation, and control systems in their activities. At present, the architecture of modern systems for performing various day-to-day business operations is usually distributed. Therefore, it is expected that special attention will be paid to the systems that function in various distributed environments.

The topics of the Special Issue include but are not limited to the following research fields:

  • Theoretical foundation of information, computing, and control systems;
  • Methods, tools, and technologies of the system development;
  • Development and application of systems in computer science;
  • Mathematical modeling of systems;
  • Control systems;
  • Decision support systems;
  • Intelligent systems;
  • Multi-agent systems;
  • Parallel and distributed computing systems;
  • Distance learning systems.

We kindly invite you to participate in this Special Issue and contribute to the development of the theory and practice of advanced information, computation, and control systems. We particularly invite authors of papers recommended at the current workshop and past workshops for submitting their manuscripts to the Special Issue of the journal Computation. However, we also invite other researchers to present their original scientific and practical results in the aforementioned fields in this Special Issue.

Dr. Igor Bychkov
Prof. Dr. Andrei Tchernykh
Dr. Alexander Feoktistov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information, computation, and control systems
  • mathematical modeling and optimization
  • parallel and distributed computing
  • environmental monitoring
  • decision-making support
  • artificial intelligence
  • Internet of Things
  • data science
  • education
  • agents

Published Papers (4 papers)

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Research

13 pages, 4275 KiB  
Article
Bioinspired Multipurpose Approach to the Sampling Problem
by Anton Tolstikhin
Computation 2023, 11(12), 254; https://doi.org/10.3390/computation11120254 - 14 Dec 2023
Viewed by 1221
Abstract
Currently, the sampling problem has gained wide popularity in the field of autonomous mobile agent control due to the wide range of practical and fundamental problems described with its framework. This paper considers a combined decentralized control strategy that incorporates both elements of [...] Read more.
Currently, the sampling problem has gained wide popularity in the field of autonomous mobile agent control due to the wide range of practical and fundamental problems described with its framework. This paper considers a combined decentralized control strategy that incorporates both elements of biologically inspired and gradient-based approaches. Its key feature is multitasking, consisting in the possibility of solving several tasks in parallel included in the sampling problem: localization and monitoring of several sources and restoration of the given level line boundaries. Full article
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24 pages, 7483 KiB  
Article
An Approach to Implementing High-Performance Computing for Problem Solving in Workflow-Based Energy Infrastructure Resilience Studies
by Alexander Feoktistov, Alexei Edelev, Andrei Tchernykh, Sergey Gorsky, Olga Basharina and Evgeniy Fereferov
Computation 2023, 11(12), 243; https://doi.org/10.3390/computation11120243 - 04 Dec 2023
Viewed by 1421
Abstract
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 [...] Read more.
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. Full article
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32 pages, 5498 KiB  
Article
Knowledge Graph Engineering Based on Semantic Annotation of Tables
by Nikita Dorodnykh and Aleksandr Yurin
Computation 2023, 11(9), 175; https://doi.org/10.3390/computation11090175 - 05 Sep 2023
Cited by 1 | Viewed by 1788
Abstract
A table is a convenient way to store, structure, and present data. Tables are an attractive knowledge source in various applications, including knowledge graph engineering. However, a lack of understanding of the semantic structure and meaning of their content may reduce the effectiveness [...] Read more.
A table is a convenient way to store, structure, and present data. Tables are an attractive knowledge source in various applications, including knowledge graph engineering. However, a lack of understanding of the semantic structure and meaning of their content may reduce the effectiveness of this process. Hence, the restoration of tabular semantics and the development of knowledge graphs based on semantically annotated tabular data are highly relevant tasks that have attracted a lot of attention in recent years. We propose a hybrid approach using heuristics and machine learning methods for the semantic annotation of relational tabular data and knowledge graph populations with specific entities extracted from the annotated tables. This paper discusses the main stages of the approach, its implementation, and performance testing. We also consider three case studies for the development of domain-specific knowledge graphs in the fields of industrial safety inspection, labor market analysis, and university activities. The evaluation results revealed that the application of our approach can be considered the initial stage for the rapid filling of domain-specific knowledge graphs based on tabular data. Full article
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15 pages, 2455 KiB  
Article
EA2-IMDG: Efficient Approach of Using an In-Memory Data Grid to Improve the Performance of Replication and Scheduling in Grid Environment Systems
by Abdo H. Guroob
Computation 2023, 11(3), 65; https://doi.org/10.3390/computation11030065 - 20 Mar 2023
Cited by 2 | Viewed by 1182
Abstract
This paper proposes a novel approach, EA2-IMDG (Efficient Approach of Using an In-Memory Data Grid) to improve the performance of replication and scheduling in grid environment systems. Grid environments are widely used for distributed computing, but they are often faced with the challenge [...] Read more.
This paper proposes a novel approach, EA2-IMDG (Efficient Approach of Using an In-Memory Data Grid) to improve the performance of replication and scheduling in grid environment systems. Grid environments are widely used for distributed computing, but they are often faced with the challenge of high data access latency and poor scalability. By utilizing an in-memory data grid (IMDG), the aim is to significantly reduce the data access latency and improve the resource utilization of the system. The approach uses the IMDG to store data in RAM, instead of on disk, allowing for faster data retrieval and processing. The IMDG is used to distribute data across multiple nodes, which helps to reduce the risk of data bottlenecks and improve the scalability of the system. To evaluate the proposed approach, a series of experiments were conducted, and its performance was compared with two baseline approaches: a centralized database and a centralized file system. The results of the experiments show that the EA2-IMDG approach improves the performance of replication and scheduling tasks by up to 90% in terms of data access latency and 50% in terms of resource utilization, respectively. These results suggest that the EA2-IMDG approach is a promising solution for improving the performance of grid environment systems. Full article
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